How much does word sense disambiguation help in sentiment analysis of micropost data?
|
|
- Brian Grant
- 7 years ago
- Views:
Transcription
1 How much does word sense disambiguation help in sentiment analysis of micropost data? Chiraag Sumanth PES Institute of Technology India Diana Inkpen University of Ottawa Canada 6th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis
2 OUTLINE Introduction What is Word Sense Disambiguation Word Sense Disambiguation and Sentiment Analysis Dataset System Description Results Error Analysis Conclusion
3 INTRODUCTION This short paper describes a sentiment analysis system for micro-post data that includes analysis of tweets from Twitter and Short Messaging Service (SMS) text messages. Use Word Sense Disambiguation techniques in sentiment analysis at the message level, where the entire tweet or SMS text was analysed to determine its dominant sentiment. Use of Word Sense Disambiguation alone has resulted in an improved sentiment analysis system that outperforms systems built without incorporating Word Sense Disambiguation.
4 What is Word Sense Disambiguation (WSD)? Any natural language processing system encounters the problem of lexical ambiguity, be it syntactic or semantic. The resolution of a word s syntactic ambiguity has largely been by part-of-speech taggers which with high levels of accuracy. The problem is that words often have more than one meaning, sometimes fairly similar and sometimes completely different. The meaning of a word in a particular usage can only be determined by examining its context. Word Sense Disambiguation (WSD) is the process of identifying the sense of such words, called polysemic words.
5 Word Sense Disambiguation and Sentiment Analysis Akkaya et al. (2009) showed that Subjectivity Word Sense Disambiguation (SWSD) improves contextual opinion analysis. By applying SWSD to contextual polarity classification, an accuracy improvement of 3 percentage points was observed over the original classifier (Wilson et al., 2005a) calculated on the SenMPQA dataset. Rentoumi et al. (2009) showed that WSD is valuable in polarity classification of sentences containing figurative expressions. The primary focus of our work was to throw light on the influence of WSD in the sentiment analysis of micropost or social-media text.
6 Dataset Dataset from Conference on Semantic Evaluation Exercises (SemEval-2013) for Task 2: Sentiment Analysis in Twitter The training dataset contained only annotated tweets and no training data for SMS text. No additional training data was used by our system. The test dataset contained both tweets and SMS texts.
7 System Description Single lexical Resource used: SentiWordNet (Baccianella et al., 2010) is a lexical resource for opinion mining. SentiWordNet assigns to each synset of WordNet three sentiment scores: positivity, negativity, and objectivity, totally adding up to 1.0. Babelfy is a unified, multilingual, graph-based approach to Entity Linking and Word Sense Disambiguation, based on the BabelNet 3.0 multilingual semantic network. Babelfy was used for WSD in our system as experiments on six gold-standard datasets show its state-of-the-art performance, as well as its robustness across languages. Preprocessing tweets: Tokenization: Carnegie Mellon University (CMU) Twitter NLP tokenizer tool Stop-word removal & word stemming: Tools provided by the NLTK. Word-segmentation for hashtags (starting with #) and user-ids (starting reinserted them after segmentation.
8 System Description For each term in the pre-processed text, retrieve the SentiWordNet scores for the matching sense of that term word, after disambiguating the sense using Babelfy. Each tweet or SMS text was represented as a vector made up of three features: The total positive score for the entire text: Aggregate the SentiWordNet Positive (P) scores for every term and divide by the total length of the text. The total negative score for the entire text: Aggregate the SentiWordNet Negative (N) scores for every term and divide by the total length of the text. The total Neutral/Objective score for the entire text: Aggregate the SentiWordNet Objective (O) scores for every term and divide by the total length of the text. This phase of the system is unsupervised where the unlabeled tweets and SMS text messages are processed and the three-featured vector representation is formed.
9 System Description The subsequent phase uses supervised learning to make the system learn how to combine these three numeric features, representing each text, and reach a decision on the sentiment of that text. We used the Random Forest classifier of Weka for this purpose. It corrects the decision trees habit of overfitting to their training set. Advantages of this approach: Large amounts of unlabelled data can be processed and the three-featured vector representation for that dataset can be constructed without any supervision or training required (in the unsupervised phase). We use only three features (P, N and O scores) in the supervised training, and also do not use datasetspecific features such as bag of words, and therefore the system is not prone to the so-called concept drift phenomenon
10 RESULTS The Precision, Recall and F-score metrics for the Twitter test data are shown. Class Precision Recall F-Score Positive Negative Neutral The Precision, Recall and F-score metrics for the SMS test data are shown. Class Precision Recall F-Score Positive Negative Neutral The official metric used for evaluating system performance was average F-score for the positive and negative class.
11 RESULTS Comparison of Average F-scores for positive/negative classes [All scores reported are for the test datasets] Dataset Tweets SMS Baseline Baseline NRC-Canada Our System Baseline 1: A majority classifier that always predicts the most frequent class as output. The second most frequent class, the positive class was chosen since the neutral class is not considered for F-score evaluation. Baseline 2: We do not disambiguate word senses, and instead the reported SentiWordNet scores of first sense of the word for the correct part-of-speech. NRC-Canada: All unigram-features only results of the system developed by NRC-Canada that was ranked first in the same task in the SemEval 2013 competition. Features included punctuation, upper-case words, POS tags, hashtags, unigram-only emotion and sentiment lexicons, emoticon detection, elongated words, and negation detection.
12 RESULTS Report an improvement over the all-unigram features score of NRC Canada: percentage points for tweets percentage points for SMS text messages Report a considerable improvement over the Baseline 2 (First sense for correct POS used): percentage points for tweets percentage points for SMS text messages We chose SemEval 2013 data and not data from the more recent editions of SemEval, because unigram-features-only score of the best scoring system (NRC-Canada) was reported in their SemEval 2013 submission. There has been no reported changes or improvements for the allunigram-features only model in the recent editions.
13 Error Analysis In both the cases of tweets and SMS text messages, the Precision and Recall for the Negative class is relatively lower. Considerably lesser samples of negative tweets in training data comprises only 15% of the training dataset. Therefore, the trained model maybe biased towards the more frequent classes, that is Positive and Neutral classes. No polarity or sentiment lexicons were used. Removal of such lexicons was reported to have the highest negative pact on performance, as observed in the NRC-Canada system Not used word n-grams or character n-grams in our system as features and this may have had a detrimental impact on performance. Our system does not feature any negation detection or encoding-detection, such as emoticons, punctuations, or upper-case letters. However, using such few features has also helped determine that the considerable improvement in performance reported can be primarily attributed only to WSD. There are no other features used in our system that can claim to have contributed to the improved performance.
14 CONCLUSION Our system that throws light on the positive influence that WSD can have when it comes to analyzing social-media and micropost data. Used only three numeric-feature vectors to represent the data for training our system and no additional features. Therefore, the considerable improvement in performance reported over NRC-Canada and the other baselines without WSD can be primarily attributed only to Word Sense Disambiguation and the P, N and O scores that are determined from the SentiWordNet lexicon as a result of disambiguating the text. The system can also work well for future data, since we are not using bag of words features, and our system is not prone to performance degradation due to concept drift. Our future work will explore the addition of several other features to the current system, in addition to the existing WSD-aided features to further improve system performance.
15 THANK YOU
VCU-TSA at Semeval-2016 Task 4: Sentiment Analysis in Twitter
VCU-TSA at Semeval-2016 Task 4: Sentiment Analysis in Twitter Gerard Briones and Kasun Amarasinghe and Bridget T. McInnes, PhD. Department of Computer Science Virginia Commonwealth University Richmond,
More informationSentiment Lexicons for Arabic Social Media
Sentiment Lexicons for Arabic Social Media Saif M. Mohammad 1, Mohammad Salameh 2, Svetlana Kiritchenko 1 1 National Research Council Canada, 2 University of Alberta saif.mohammad@nrc-cnrc.gc.ca, msalameh@ualberta.ca,
More informationA Comparative Study on Sentiment Classification and Ranking on Product Reviews
A Comparative Study on Sentiment Classification and Ranking on Product Reviews C.EMELDA Research Scholar, PG and Research Department of Computer Science, Nehru Memorial College, Putthanampatti, Bharathidasan
More informationKea: Expression-level Sentiment Analysis from Twitter Data
Kea: Expression-level Sentiment Analysis from Twitter Data Ameeta Agrawal Computer Science and Engineering York University Toronto, Canada ameeta@cse.yorku.ca Aijun An Computer Science and Engineering
More informationSentiment Analysis: a case study. Giuseppe Castellucci castellucci@ing.uniroma2.it
Sentiment Analysis: a case study Giuseppe Castellucci castellucci@ing.uniroma2.it Web Mining & Retrieval a.a. 2013/2014 Outline Sentiment Analysis overview Brand Reputation Sentiment Analysis in Twitter
More informationSentiment Analysis of Microblogs
Thesis for the degree of Master in Language Technology Sentiment Analysis of Microblogs Tobias Günther Supervisor: Richard Johansson Examiner: Prof. Torbjörn Lager June 2013 Contents 1 Introduction 3 1.1
More informationImproving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training
Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training Bing Xiang * IBM Watson 1101 Kitchawan Rd Yorktown Heights, NY 10598, USA bingxia@us.ibm.com Liang Zhou
More informationSentiment analysis: towards a tool for analysing real-time students feedback
Sentiment analysis: towards a tool for analysing real-time students feedback Nabeela Altrabsheh Email: nabeela.altrabsheh@port.ac.uk Mihaela Cocea Email: mihaela.cocea@port.ac.uk Sanaz Fallahkhair Email:
More informationIIT Patna: Supervised Approach for Sentiment Analysis in Twitter
IIT Patna: Supervised Approach for Sentiment Analysis in Twitter Raja Selvarajan and Asif Ekbal Department of Computer Science and Engineering Indian Institute of Technology Patna, India {raja.cs10,asif}@iitp.ac.in
More informationMicroblog Sentiment Analysis with Emoticon Space Model
Microblog Sentiment Analysis with Emoticon Space Model Fei Jiang, Yiqun Liu, Huanbo Luan, Min Zhang, and Shaoping Ma State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory
More informationUsing an Emotion-based Model and Sentiment Analysis Techniques to Classify Polarity for Reputation
Using an Emotion-based Model and Sentiment Analysis Techniques to Classify Polarity for Reputation Jorge Carrillo de Albornoz, Irina Chugur, and Enrique Amigó Natural Language Processing and Information
More informationSentiment Analysis. D. Skrepetos 1. University of Waterloo. NLP Presenation, 06/17/2015
Sentiment Analysis D. Skrepetos 1 1 Department of Computer Science University of Waterloo NLP Presenation, 06/17/2015 D. Skrepetos (University of Waterloo) Sentiment Analysis NLP Presenation, 06/17/2015
More informationIIIT-H at SemEval 2015: Twitter Sentiment Analysis The good, the bad and the neutral!
IIIT-H at SemEval 2015: Twitter Sentiment Analysis The good, the bad and the neutral! Ayushi Dalmia, Manish Gupta, Vasudeva Varma Search and Information Extraction Lab International Institute of Information
More informationCMUQ@Qatar:Using Rich Lexical Features for Sentiment Analysis on Twitter
CMUQ@Qatar:Using Rich Lexical Features for Sentiment Analysis on Twitter Sabih Bin Wasi, Rukhsar Neyaz, Houda Bouamor, Behrang Mohit Carnegie Mellon University in Qatar {sabih, rukhsar, hbouamor, behrang}@cmu.edu
More informationSentiment Analysis of Twitter Data
Sentiment Analysis of Twitter Data Apoorv Agarwal Boyi Xie Ilia Vovsha Owen Rambow Rebecca Passonneau Department of Computer Science Columbia University New York, NY 10027 USA {apoorv@cs, xie@cs, iv2121@,
More informationRobust Sentiment Detection on Twitter from Biased and Noisy Data
Robust Sentiment Detection on Twitter from Biased and Noisy Data Luciano Barbosa AT&T Labs - Research lbarbosa@research.att.com Junlan Feng AT&T Labs - Research junlan@research.att.com Abstract In this
More informationCombining Social Data and Semantic Content Analysis for L Aquila Social Urban Network
I-CiTies 2015 2015 CINI Annual Workshop on ICT for Smart Cities and Communities Palermo (Italy) - October 29-30, 2015 Combining Social Data and Semantic Content Analysis for L Aquila Social Urban Network
More informationApproaches for Sentiment Analysis on Twitter: A State-of-Art study
Approaches for Sentiment Analysis on Twitter: A State-of-Art study Harsh Thakkar and Dhiren Patel Department of Computer Engineering, National Institute of Technology, Surat-395007, India {harsh9t,dhiren29p}@gmail.com
More informationTwitter Stock Bot. John Matthew Fong The University of Texas at Austin jmfong@cs.utexas.edu
Twitter Stock Bot John Matthew Fong The University of Texas at Austin jmfong@cs.utexas.edu Hassaan Markhiani The University of Texas at Austin hassaan@cs.utexas.edu Abstract The stock market is influenced
More informationChapter 8. Final Results on Dutch Senseval-2 Test Data
Chapter 8 Final Results on Dutch Senseval-2 Test Data The general idea of testing is to assess how well a given model works and that can only be done properly on data that has not been seen before. Supervised
More informationSentiment analysis for news articles
Prashant Raina Sentiment analysis for news articles Wide range of applications in business and public policy Especially relevant given the popularity of online media Previous work Machine learning based
More informationImpact of Financial News Headline and Content to Market Sentiment
International Journal of Machine Learning and Computing, Vol. 4, No. 3, June 2014 Impact of Financial News Headline and Content to Market Sentiment Tan Li Im, Phang Wai San, Chin Kim On, Rayner Alfred,
More informationASVUniOfLeipzig: Sentiment Analysis in Twitter using Data-driven Machine Learning Techniques
ASVUniOfLeipzig: Sentiment Analysis in Twitter using Data-driven Machine Learning Techniques Robert Remus Natural Language Processing Group, Department of Computer Science, University of Leipzig, Germany
More informationSearch and Data Mining: Techniques. Text Mining Anya Yarygina Boris Novikov
Search and Data Mining: Techniques Text Mining Anya Yarygina Boris Novikov Introduction Generally used to denote any system that analyzes large quantities of natural language text and detects lexical or
More informationSENTIMENT ANALYSIS USING BIG DATA FROM SOCIALMEDIA
SENTIMENT ANALYSIS USING BIG DATA FROM SOCIALMEDIA 1 VAISHALI SARATHY, 2 SRINIDHI.S, 3 KARTHIKA.S 1,2,3 Department of Information Technology, SSN College of Engineering Old Mahabalipuram Road, alavakkam
More informationNILC USP: A Hybrid System for Sentiment Analysis in Twitter Messages
NILC USP: A Hybrid System for Sentiment Analysis in Twitter Messages Pedro P. Balage Filho and Thiago A. S. Pardo Interinstitutional Center for Computational Linguistics (NILC) Institute of Mathematical
More informationDiegoLab16 at SemEval-2016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment Lexicons
DiegoLab16 at SemEval-016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment Lexicons Abeed Sarker and Graciela Gonzalez Department of Biomedical Informatics Arizona State University
More informationA comparison of Lexicon-based approaches for Sentiment Analysis of microblog posts
A comparison of Lexicon-based approaches for Sentiment Analysis of microblog posts Cataldo Musto, Giovanni Semeraro, Marco Polignano Department of Computer Science University of Bari Aldo Moro, Italy {cataldo.musto,giovanni.semeraro,marco.polignano}@uniba.it
More informationBuilding the Multilingual Web of Data: A Hands-on tutorial (ISWC 2014, Riva del Garda - Italy)
Building the Multilingual Web of Data: A Hands-on tutorial (ISWC 2014, Riva del Garda - Italy) Multilingual Word Sense Disambiguation and Entity Linking on the Web based on BabelNet Roberto Navigli, Tiziano
More informationKea: Sentiment Analysis of Phrases Within Short Texts
Kea: Sentiment Analysis of Phrases Within Short Texts Ameeta Agrawal, Aijun An Department of Computer Science and Engineering York University, Toronto, Canada M3J 1P3 {ameeta, aan}@cse.yorku.ca Abstract
More informationMeta-Level Sentiment Models for Big Social Data Analysis
Meta-Level Sentiment Models for Big Social Data Analysis Felipe Bravo-Marquez a,b,, Marcelo Mendoza c, Barbara Poblete d a Department of Computer Science, The University of Waikato, Private Bag 3, Hamilton
More informationSemantic Sentiment Analysis of Twitter
Semantic Sentiment Analysis of Twitter Hassan Saif, Yulan He & Harith Alani Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom The 11 th International Semantic Web Conference
More informationBing Liu. Web Data Mining. Exploring Hyperlinks, Contents, and Usage Data. With 177 Figures. ~ Spring~r
Bing Liu Web Data Mining Exploring Hyperlinks, Contents, and Usage Data With 177 Figures ~ Spring~r Table of Contents 1. Introduction.. 1 1.1. What is the World Wide Web? 1 1.2. ABrief History of the Web
More informationEffect of Using Regression on Class Confidence Scores in Sentiment Analysis of Twitter Data
Effect of Using Regression on Class Confidence Scores in Sentiment Analysis of Twitter Data Itir Onal *, Ali Mert Ertugrul, Ruken Cakici * * Department of Computer Engineering, Middle East Technical University,
More informationTwitter Sentiment Analysis of Movie Reviews using Machine Learning Techniques.
Twitter Sentiment Analysis of Movie Reviews using Machine Learning Techniques. Akshay Amolik, Niketan Jivane, Mahavir Bhandari, Dr.M.Venkatesan School of Computer Science and Engineering, VIT University,
More informationA Method for Automatic De-identification of Medical Records
A Method for Automatic De-identification of Medical Records Arya Tafvizi MIT CSAIL Cambridge, MA 0239, USA tafvizi@csail.mit.edu Maciej Pacula MIT CSAIL Cambridge, MA 0239, USA mpacula@csail.mit.edu Abstract
More informationData Mining on Social Networks. Dionysios Sotiropoulos Ph.D.
Data Mining on Social Networks Dionysios Sotiropoulos Ph.D. 1 Contents What are Social Media? Mathematical Representation of Social Networks Fundamental Data Mining Concepts Data Mining Tasks on Digital
More informationSentiment Analysis of Short Informal Texts
Journal of Artificial Intelligence Research 50 (2014) 723 762 Submitted 12/13; published 08/14 Sentiment Analysis of Short Informal Texts Svetlana Kiritchenko Xiaodan Zhu Saif M. Mohammad National Research
More informationFine-grained German Sentiment Analysis on Social Media
Fine-grained German Sentiment Analysis on Social Media Saeedeh Momtazi Information Systems Hasso-Plattner-Institut Potsdam University, Germany Saeedeh.momtazi@hpi.uni-potsdam.de Abstract Expressing opinions
More informationSentiment Analysis Tool using Machine Learning Algorithms
Sentiment Analysis Tool using Machine Learning Algorithms I.Hemalatha 1, Dr. G. P Saradhi Varma 2, Dr. A.Govardhan 3 1 Research Scholar JNT University Kakinada, Kakinada, A.P., INDIA 2 Professor & Head,
More informationTwitter Sentiment Analysis
Twitter Sentiment Analysis By Afroze Ibrahim Baqapuri (NUST-BEE-310) A Project report submitted in fulfilment of the requirement for the degree of Bachelors in Electrical (Electronics) Engineering Department
More informationSentiment Analysis for Movie Reviews
Sentiment Analysis for Movie Reviews Ankit Goyal, a3goyal@ucsd.edu Amey Parulekar, aparulek@ucsd.edu Introduction: Movie reviews are an important way to gauge the performance of a movie. While providing
More informationSENTIMENT ANALYSIS: A STUDY ON PRODUCT FEATURES
University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Dissertations and Theses from the College of Business Administration Business Administration, College of 4-1-2012 SENTIMENT
More informationExtracting Opinions and Facts for Business Intelligence
Extracting Opinions and Facts for Business Intelligence Horacio Saggion, Adam Funk Department of Computer Science University of Sheffield Regent Court 211 Portobello Street Sheffield - S1 5DP {H.Saggion,A.Funk}@dcs.shef.ac.uk
More informationDeveloping a Successful SemEval Task in Sentiment Analysis of Twitter and Other Social Media Texts
Noname manuscript No. (will be inserted by the editor) Developing a Successful SemEval Task in Sentiment Analysis of Twitter and Other Social Media Texts Preslav Nakov Sara Rosenthal Svetlana Kiritchenko
More informationSentiment Analysis: Incremental learning to build domain models
Sentiment Analysis: Incremental learning to build domain models Raimon Bosch Master thesis, Universitat Pompeu Fabra (2013) Intelligent Interactive Systems Prof. Dr. Leo Wanner, Department of Information
More informationCIRGIRDISCO at RepLab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet
CIRGIRDISCO at RepLab2014 Reputation Dimension Task: Using Wikipedia Graph Structure for Classifying the Reputation Dimension of a Tweet Muhammad Atif Qureshi 1,2, Arjumand Younus 1,2, Colm O Riordan 1,
More informationLearning to Identify Emotions in Text
Learning to Identify Emotions in Text Carlo Strapparava FBK-Irst, Italy strappa@itc.it Rada Mihalcea University of North Texas rada@cs.unt.edu ABSTRACT This paper describes experiments concerned with the
More informationTowards SoMEST Combining Social Media Monitoring with Event Extraction and Timeline Analysis
Towards SoMEST Combining Social Media Monitoring with Event Extraction and Timeline Analysis Yue Dai, Ernest Arendarenko, Tuomo Kakkonen, Ding Liao School of Computing University of Eastern Finland {yvedai,
More informationSentiment analysis of Twitter microblogging posts. Jasmina Smailović Jožef Stefan Institute Department of Knowledge Technologies
Sentiment analysis of Twitter microblogging posts Jasmina Smailović Jožef Stefan Institute Department of Knowledge Technologies Introduction Popularity of microblogging services Twitter microblogging posts
More informationLexical and Machine Learning approaches toward Online Reputation Management
Lexical and Machine Learning approaches toward Online Reputation Management Chao Yang, Sanmitra Bhattacharya, and Padmini Srinivasan Department of Computer Science, University of Iowa, Iowa City, IA, USA
More informationMicro blogs Oriented Word Segmentation System
Micro blogs Oriented Word Segmentation System Yijia Liu, Meishan Zhang, Wanxiang Che, Ting Liu, Yihe Deng Research Center for Social Computing and Information Retrieval Harbin Institute of Technology,
More informationForecasting stock markets with Twitter
Forecasting stock markets with Twitter Argimiro Arratia argimiro@lsi.upc.edu Joint work with Marta Arias and Ramón Xuriguera To appear in: ACM Transactions on Intelligent Systems and Technology, 2013,
More informationSentiment Analysis Using Dependency Trees and Named-Entities
Proceedings of the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference Sentiment Analysis Using Dependency Trees and Named-Entities Ugan Yasavur, Jorge Travieso, Christine
More informationUT-DB: An Experimental Study on Sentiment Analysis in Twitter
UT-DB: An Experimental Study on Sentiment Analysis in Twitter Zhemin Zhu Djoerd Hiemstra Peter Apers Andreas Wombacher CTIT Database Group, University of Twente Drienerlolaan 5, 7500 AE, Enschede, The
More informationHow To Monitor A Company'S Reputation On Twitter
Overview of RepLab 2012: Evaluating Online Reputation Management Systems Enrique Amigó, Adolfo Corujo, Julio Gonzalo, Edgar Meij, and Maarten de Rijke enrique@lsi.uned.es,acorujo@llorenteycuenca.com, julio@lsi.uned.es,edgar.meij@uva.nl,derijke@uva.nl
More informationKnowledge-Based WSD on Specific Domains: Performing Better than Generic Supervised WSD
Knowledge-Based WSD on Specific Domains: Performing Better than Generic Supervised WSD Eneko Agirre and Oier Lopez de Lacalle and Aitor Soroa Informatika Fakultatea, University of the Basque Country 20018,
More informationTable of Contents. Chapter No. 1 Introduction 1. iii. xiv. xviii. xix. Page No.
Table of Contents Title Declaration by the Candidate Certificate of Supervisor Acknowledgement Abstract List of Figures List of Tables List of Abbreviations Chapter Chapter No. 1 Introduction 1 ii iii
More informationDoctoral Consortium 2013 Dept. Lenguajes y Sistemas Informáticos UNED
Doctoral Consortium 2013 Dept. Lenguajes y Sistemas Informáticos UNED 17 19 June 2013 Monday 17 June Salón de Actos, Facultad de Psicología, UNED 15.00-16.30: Invited talk Eneko Agirre (Euskal Herriko
More informationSAIL: Sentiment Analysis using Semantic Similarity and Contrast Features
SAIL: Sentiment Analysis using Semantic Similarity and Contrast Features Nikolaos Malandrakis, Michael Falcone, Colin Vaz, Jesse Bisogni, Alexandros Potamianos, Shrikanth Narayanan Signal Analysis and
More informationSome Experiments on Modeling Stock Market Behavior Using Investor Sentiment Analysis and Posting Volume from Twitter
Some Experiments on Modeling Stock Market Behavior Using Investor Sentiment Analysis and Posting Volume from Twitter Nuno Oliveira Centro Algoritmi Dep. Sistemas de Informação Universidade do Minho 4800-058
More informationIdentifying Focus, Techniques and Domain of Scientific Papers
Identifying Focus, Techniques and Domain of Scientific Papers Sonal Gupta Department of Computer Science Stanford University Stanford, CA 94305 sonal@cs.stanford.edu Christopher D. Manning Department of
More informationA Joint Model of Feature Mining and Sentiment Analysis for Product Review Rating
A Joint Model of Feature Mining and Sentiment Analysis for Product Review Rating Jorge Carrillo de Albornoz, Laura Plaza, Pablo Gervás, and Alberto Díaz Universidad Complutense de Madrid, Departamento
More informationSentiment Classification on Polarity Reviews: An Empirical Study Using Rating-based Features
Sentiment Classification on Polarity Reviews: An Empirical Study Using Rating-based Features Dai Quoc Nguyen and Dat Quoc Nguyen and Thanh Vu and Son Bao Pham Faculty of Information Technology University
More informationEffectiveness of term weighting approaches for sparse social media text sentiment analysis
MSc in Computing, Business Intelligence and Data Mining stream. MSc Research Project Effectiveness of term weighting approaches for sparse social media text sentiment analysis Submitted by: Mulluken Wondie,
More informationEmoticon Smoothed Language Models for Twitter Sentiment Analysis
Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence Emoticon Smoothed Language Models for Twitter Sentiment Analysis Kun-Lin Liu, Wu-Jun Li, Minyi Guo Shanghai Key Laboratory of
More informationCI6227: Data Mining. Lesson 11b: Ensemble Learning. Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore.
CI6227: Data Mining Lesson 11b: Ensemble Learning Sinno Jialin PAN Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore Acknowledgements: slides are adapted from the lecture notes
More informationEmail Spam Detection A Machine Learning Approach
Email Spam Detection A Machine Learning Approach Ge Song, Lauren Steimle ABSTRACT Machine learning is a branch of artificial intelligence concerned with the creation and study of systems that can learn
More informationSU-FMI: System Description for SemEval-2014 Task 9 on Sentiment Analysis in Twitter
SU-FMI: System Description for SemEval-2014 Task 9 on Sentiment Analysis in Twitter Boris Velichkov, Borislav Kapukaranov, Ivan Grozev, Jeni Karanesheva, Todor Mihaylov, Yasen Kiprov, Georgi Georgiev,
More informationCredit Card Fraud Detection and Concept-Drift Adaptation with Delayed Supervised Information
Credit Card Fraud Detection and Concept-Drift Adaptation with Delayed Supervised Information Andrea Dal Pozzolo, Giacomo Boracchi, Olivier Caelen, Cesare Alippi, and Gianluca Bontempi 15/07/2015 IEEE IJCNN
More informationEFFICIENTLY PROVIDE SENTIMENT ANALYSIS DATA SETS USING EXPRESSIONS SUPPORT METHOD
EFFICIENTLY PROVIDE SENTIMENT ANALYSIS DATA SETS USING EXPRESSIONS SUPPORT METHOD 1 Josephine Nancy.C, 2 K Raja. 1 PG scholar,department of Computer Science, Tagore Institute of Engineering and Technology,
More informationSentiment analysis on news articles using Natural Language Processing and Machine Learning Approach.
Sentiment analysis on news articles using Natural Language Processing and Machine Learning Approach. Pranali Chilekar 1, Swati Ubale 2, Pragati Sonkambale 3, Reema Panarkar 4, Gopal Upadhye 5 1 2 3 4 5
More informationSentiCircles for Contextual and Conceptual Semantic Sentiment Analysis of Twitter
SentiCircles for Contextual and Conceptual Semantic Sentiment Analysis of Twitter Hassan Saif, 1 Miriam Fernandez, 1 Yulan He 2 and Harith Alani 1 1 Knowledge Media Institute, The Open University, United
More informationCustomer Intentions Analysis of Twitter Based on Semantic Patterns
Customer Intentions Analysis of Twitter Based on Semantic Patterns Mohamed Hamroun mohamed.hamrounn@gmail.com Mohamed Salah Gouider ms.gouider@yahoo.fr Lamjed Ben Said lamjed.bensaid@isg.rnu.tn ABSTRACT
More informationONLINE RESUME PARSING SYSTEM USING TEXT ANALYTICS
ONLINE RESUME PARSING SYSTEM USING TEXT ANALYTICS Divyanshu Chandola 1, Aditya Garg 2, Ankit Maurya 3, Amit Kushwaha 4 1 Student, Department of Information Technology, ABES Engineering College, Uttar Pradesh,
More informationA Sentiment Analysis Model Integrating Multiple Algorithms and Diverse. Features. Thesis
A Sentiment Analysis Model Integrating Multiple Algorithms and Diverse Features Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The
More informationDIY Social Sentiment Analysis in 3 Steps
DIY Social Sentiment Analysis in 3 Steps Feb 26, 2015 About NetElixir Mission: To Help Digital Marketers Succeed Online. Incorporated: 2005. Global Offices: Princeton (HQ). London. Hyderabad. Team: 75+
More informationContent vs. Context for Sentiment Analysis: a Comparative Analysis over Microblogs
Content vs. Context for Sentiment Analysis: a Comparative Analysis over Microblogs Fotis Aisopos $, George Papadakis $,, Konstantinos Tserpes $, Theodora Varvarigou $ L3S Research Center, Germany papadakis@l3s.de
More informationSI485i : NLP. Set 6 Sentiment and Opinions
SI485i : NLP Set 6 Sentiment and Opinions It's about finding out what people think... Can be big business Someone who wants to buy a camera Looks for reviews online Someone who just bought a camera Writes
More informationAn Information Gain-Driven Feature Study for Aspect-Based Sentiment Analysis
An Information Gain-Driven Feature Study for Aspect-Based Sentiment Analysis Kim Schouten, Flavius Frasincar, and Rommert Dekker Erasmus University Rotterdam Burgemeester Oudlaan 50, 3062 PA Rotterdam,
More informationII. RELATED WORK. Sentiment Mining
Sentiment Mining Using Ensemble Classification Models Matthew Whitehead and Larry Yaeger Indiana University School of Informatics 901 E. 10th St. Bloomington, IN 47408 {mewhiteh, larryy}@indiana.edu Abstract
More informationWeb Mining. Margherita Berardi LACAM. Dipartimento di Informatica Università degli Studi di Bari berardi@di.uniba.it
Web Mining Margherita Berardi LACAM Dipartimento di Informatica Università degli Studi di Bari berardi@di.uniba.it Bari, 24 Aprile 2003 Overview Introduction Knowledge discovery from text (Web Content
More informationTweetAlert: Semantic Analytics in Social Networks for Citizen Opinion Mining in the City of the Future
TweetAlert: Semantic Analytics in Social Networks for Citizen Opinion Mining in the City of the Future Julio Villena-Román 1,2, Adrián Luna-Cobos 1,3, José Carlos González-Cristóbal 3,1 1 DAEDALUS - Data,
More informationThe Linguistics of Sentiment Analysis
Portland State University PDXScholar University Honors Theses University Honors College 5-24-2013 The Linguistics of Sentiment Analysis Laurel Hart Portland State University Let us know how access to this
More informationWeb Document Clustering
Web Document Clustering Lab Project based on the MDL clustering suite http://www.cs.ccsu.edu/~markov/mdlclustering/ Zdravko Markov Computer Science Department Central Connecticut State University New Britain,
More informationUNED Online Reputation Monitoring Team at RepLab 2013
UNED Online Reputation Monitoring Team at RepLab 2013 Damiano Spina, Jorge Carrillo-de-Albornoz, Tamara Martín, Enrique Amigó, Julio Gonzalo, and Fernando Giner {damiano,jcalbornoz,tmartin,enrique,julio}@lsi.uned.es,
More informationWeb Information Mining and Decision Support Platform for the Modern Service Industry
Web Information Mining and Decision Support Platform for the Modern Service Industry Binyang Li 1,2, Lanjun Zhou 2,3, Zhongyu Wei 2,3, Kam-fai Wong 2,3,4, Ruifeng Xu 5, Yunqing Xia 6 1 Dept. of Information
More informationSocial Media Data Mining and Inference system based on Sentiment Analysis
Social Media Data Mining and Inference system based on Sentiment Analysis Master of Science Thesis in Applied Information Technology ANA SUFIAN RANJITH ANANTHARAMAN Department of Applied Information Technology
More informationSentiment Analysis of Movie Reviews and Twitter Statuses. Introduction
Sentiment Analysis of Movie Reviews and Twitter Statuses Introduction Sentiment analysis is the task of identifying whether the opinion expressed in a text is positive or negative in general, or about
More informationContext Aware Predictive Analytics: Motivation, Potential, Challenges
Context Aware Predictive Analytics: Motivation, Potential, Challenges Mykola Pechenizkiy Seminar 31 October 2011 University of Bournemouth, England http://www.win.tue.nl/~mpechen/projects/capa Outline
More informationData Mining Yelp Data - Predicting rating stars from review text
Data Mining Yelp Data - Predicting rating stars from review text Rakesh Chada Stony Brook University rchada@cs.stonybrook.edu Chetan Naik Stony Brook University cnaik@cs.stonybrook.edu ABSTRACT The majority
More informationUniversity of Glasgow Terrier Team / Project Abacá at RepLab 2014: Reputation Dimensions Task
University of Glasgow Terrier Team / Project Abacá at RepLab 2014: Reputation Dimensions Task Graham McDonald, Romain Deveaud, Richard McCreadie, Timothy Gollins, Craig Macdonald and Iadh Ounis School
More informationData Mining for Tweet Sentiment Classification
Master Thesis Data Mining for Tweet Sentiment Classification Author: Internal Supervisors: R. de Groot dr. A.J. Feelders r.degroot4@students.uu.nl prof. dr. A.P.J.M. Siebes External Supervisors: E. Drenthen
More informationEnd-to-End Sentiment Analysis of Twitter Data
End-to-End Sentiment Analysis of Twitter Data Apoor v Agarwal 1 Jasneet Singh Sabharwal 2 (1) Columbia University, NY, U.S.A. (2) Guru Gobind Singh Indraprastha University, New Delhi, India apoorv@cs.columbia.edu,
More informationOverview of RepLab 2013: Evaluating Online Reputation Monitoring Systems
Overview of RepLab 2013: Evaluating Online Reputation Monitoring Systems Enrique Amigó 1, Jorge Carrillo de Albornoz 1, Irina Chugur 1, Adolfo Corujo 2, Julio Gonzalo 1, Tamara Martín 1, Edgar Meij 3,
More informationSentiment analysis on tweets in a financial domain
Sentiment analysis on tweets in a financial domain Jasmina Smailović 1,2, Miha Grčar 1, Martin Žnidaršič 1 1 Dept of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia 2 Jožef Stefan International
More informationCombining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis
Combining Lexicon-based and Learning-based Methods for Twitter Sentiment Analysis Lei Zhang, Riddhiman Ghosh, Mohamed Dekhil, Meichun Hsu, Bing Liu HP Laboratories HPL-2011-89 Abstract: With the booming
More informationTerminology Extraction from Log Files
Terminology Extraction from Log Files Hassan Saneifar 1,2, Stéphane Bonniol 2, Anne Laurent 1, Pascal Poncelet 1, and Mathieu Roche 1 1 LIRMM - Université Montpellier 2 - CNRS 161 rue Ada, 34392 Montpellier
More informationSemi-Supervised Learning for Blog Classification
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008) Semi-Supervised Learning for Blog Classification Daisuke Ikeda Department of Computational Intelligence and Systems Science,
More informationSentiment Analysis on Twitter with Stock Price and Significant Keyword Correlation. Abstract
Sentiment Analysis on Twitter with Stock Price and Significant Keyword Correlation Linhao Zhang Department of Computer Science, The University of Texas at Austin (Dated: April 16, 2013) Abstract Though
More information