Sentiment Analysis in Twitter

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1 Sentiment Analysis in Twitter Maria Karanasou, Christos Doulkeridis, Maria Halkidi Department of Digital Systems School of Information and Communication Technologies University of Piraeus Half-day RoadRunner Workshop, Univ.Piraeus, 2/6/2015

2 What is Sentiment Analysis Sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials [Wikipedia] Applications Product reviews Track customer satisfaction Microblogging, public opinion 2

3 Sentiment Analysis of Figurative Objective Language in Twitter (SemEval2015 Task#11) Given a labeled train set that contains figurative tweets, correctly predict how positive and how negative is a given tweet that may contain figurative speech on a scale from -5 to 5 3

4 Overview of Our Approach The proposed system consists of two main modules: (a) the preprocessing, and (b) the classification module Each tweet t was submitted to preprocessing, in order to remove useless information and extract the desired/targeted features f The result of the preprocessing of a given tweet t consists of a feature dictionary (fd) that stores the values calculated for each feature In the classification part, the feature dictionaries are converted to vectors and the result matrix is converted to a term-frequency matrix 4

5 Pre-processing Cleaning Removal of: URLS Hashtags Emoticons non-word characters stop-w Word split Correction of words that contain more than two characters in a row 5

6 Pre-processing Feature Extraction 6

7 Pre-processing Feature Extraction Given that the word wi occurs j times in the SentiWordNet corpus: The value simt is calculated as the maximum similarity score of every combination of two words and their synonyms 7

8 Classification 8

9 Experimental Datasets 8529 out of 9000 tweets in total were retrieved, 7606 from the trial set and 923 from the test set Out of these data sets, positive tweets in total are 8,2%, negative tweets are 85,2% and neutral 6,6%. For the final submission total 8529 tweets were used as a train set. The test set consisted of 4000 tweets. 9

10 Experimental Evaluation Incrementally add features to conclude to most useful (marked with (*) in previous table) Different classifiers N.Bayes, Decision Tree, SVM Discretization to swnscorewi as follows 10

11 Experimental Results Cosine Similarity MSE Overall Sarcasm Irony Metaphor Other Rank Among ironic, sarcastic, metaphoric and others, the best results were achieved in tweets containing irony and sarcasm 11

12 Results with different Classifiers Classification Decision Tree Naïve Bayes SVM Trials/ Final T F T F T F Cosine Similarity Accuracy Results with the features selected for the final submission SVM performed better both in accuracy and cosine similarity 12

13 Discussion The proposed system combines structured knowledge sources along with common tweet and figurative text features A supervised learning approach is followed, having as goal to classify tweets containing irony and metaphors The most useful features for learning are pos-tags, SentiWordNet score, text semantic similarity and hashtags Our study shows that the performance of our system could be improved by adding features related to metaphor and considering better use of hashtags in the classification process 13

14 Future Work Improve the performance of sentiment analysis Tackle the scalability challenge Real-time (streaming) data 14

15 References Maria Karanasou, Christos Doulkeridis and Maria Halkidi. DsUniPi: An SVM-based approach for Sentiment Analysis of Figurative Language on Twitter. In Proceedings of 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, June 4-5,

16 Acknowledgements This research has been co-financed by the European Union (European Social Fund ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: Aristeia II. Investing in knowledge society through the European Social Fund. 16

17 Thank you for your attention More info: Contact: 17

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