FEATURE BASED OPINION MINING FOR CUSTOMER REVIEWS

Transcription

1 I J I T E ISSN: (1-2), 2012, pp FEATURE BASED OPINION MINING FOR CUSTOMER REVIEWS G. VINODHINI *, L. SRISUBHA**, RM. CHANDRASEKARAN*** *Assistant professor, **P. G. Student, ***Professor Dept. of Computer Science, Annamalai University, Annamalai Nagar s: g.t.vino@gmail.com, l.srisubha@gmail.com, aurmc@sify.com Abstract: Now-a-days, Customers are often forced to wade through many online reviews in order to make an informed product choice. Scanning all of these reviews would be tedious, time consuming, boring and fruitless. It would be good if these reviews could be processed automatically and customers are providing with the limited generalized information. So a need arises to find out who feels how on which aspect of the product. Product feature extraction is critical to opinion mining, because its effectiveness significantly affects the performance of opinion orientation identification, as well as the ultimate effectiveness of sentiment analysis. Therefore, this work tried to find out product feature extraction from customer reviews. The main objective of this paper is to discuss about developing an information extraction system which mines customer reviews in order to build a model to extract important product feature and their evaluation by reviewers. The evaluation of the proposed work is presented based on processing the online product reviews for five different fast moving products. Keywords: opinion mining; Product feature extraction; Information extraction; customer reviews I. INTRODUCTION Opinion Mining (OM), usually called Sentiment Analysis, is a field of Web Content Mining that aims to find valuable information out of users opinions. OM is concerned with applying computational methods for the detection and measurement of opinion, sentiment and subjectivity in text [4]. A text document can be seen as a collection of objective and subjective statements, where objective statements refer to factual information present in text, and subjectivity relates to the expression of opinions, evaluations and speculations. Mining opinions on the web is a fairly new subject, and its importance has grown significantly mainly due to the fast growth of e-commerce, blogs and forums [6]. Feature based opinion mining is one of the basic tasks in OM is classifying the polarity of a given text or feature/aspect level to find out whether it is positive, negative or neutral. Different methodologies are used for this purpose. Some expert analysts used the scaling system to associate numbers with appropriate sentiments that a word is depicting. Research has also shown that subjectivity or objectivity identification can also achieve the purpose [1]. However the most fine grained analysis model would be the feature or aspect based sentiment mining method for this purpose. The basic idea of feature based opinion mining is to determine the sentiments or opinions that are expressed on different features or aspects of entities [5]. When text is classified at document level or sentence level it might not tell what the opinion holder likes or dislikes. If a document is positive on an object it clearly does not mean that the opinion holder will hold positive opinions about all the aspects or features of the object [3]. Similarly if a document is negative it does not mean that the opinion holder will dislike everything about the object described. There are generally three key types of tasks involved in feature based opinion mining: Identifying the object features, determining the opinion orientation and grouping synonyms [6]. II. BACKGROUND Much research exists on sentiment analysis of user opinion data, which mainly judges the polarities of user reviews. In these studies, sentiment

2 74 G. Vinodhini, L. Srisubha and R. M. Chandrasekaran analysis is often conducted at one of three levels: the document level, sentence level, or attribute level. Due to the increasing amount of opinions and reviews on the Internet, opinion mining has become a hot topic in data mining, in which extracting opinion features is a key step. Sentiment analysis at both the document level and sentence level has been too coarse to determine precisely what users like or dislike [2]. In order to address this problem, sentiment analysis at the attribute level is aimed at extracting opinions on products specific attributes from reviews. As mentioned, this study concentrates on product feature extraction from consumer reviews. This work review existing product feature extraction techniques and discuss their inherent limitations to highlight the motivation of this study. Existing product feature extraction techniques can broadly be classified into two major approaches: supervised and unsupervised. Supervised product feature extraction techniques require a set of preannotated review sentences as training examples [4]. A supervised learning method is then applied to construct an extraction model, which is capable of identifying product features from new consumer reviews. For example, Wong and Lam (2005, 2008) employ Hidden Markov Models and Conditional Random Fields, respectively, as the underlying learning method for extracting product features from auction websites. Although the supervised techniques can achieve reasonable effectiveness, preparing training examples is time consuming. In addition, the effectiveness of the supervised techniques greatly depends on the representativeness of the training examples. Khairullah Khan et al. (2010) proposed a novel idea to find features of product from user review in an efficient way from text through auxiliary verbs (AV) {is, was, are, were, has, have, had}. They categorized the sentences of each review into two groups by using simple rule-based approach. Group one includes those sentences which have any of the given AVs. They represented this category as sentences with auxiliary verbs (SAV). While in the other group all those sentences were included which were not in group one and called sentences without auxiliary verbs (SWAV). From the results of the experiments, they found that 82% of features and 85% of opinion-oriented sentences include AVs. Thus these AVs are good indicators of features and opinion orientation in customer reviews. Gamgarn Somprasertsri (2010) dedicated their work to properly identify the semantic relationships between product features and opinions. They proposed an approach for mining product feature and opinion based on the consideration of syntactic information and semantic information by applying dependency relations and ontological knowledge with probabilistic based model. Bing Xu, Tie-Jun Zhao (2010) analysed a Conditional Random Fields model based Chinese product features identification approach, integrating the rich features in the model, including word features, part-of-speech features, context features, chunk features and heuristic information. With the participation of every feature, the experiment results improve step by step. So the results indicate the techniques are effective in identification task. Product feature extraction is an important task of review mining and summarization. The task of product feature extraction is to find product features that customers refer to in their topic reviews. It would be useful to characterize the opinions which they review or express about the products. Pattarachai Lalitrojwong (2008) proposed an approach to product feature extraction using a maximum entropy model. Maximum entropy is a probability distribution estimation technique. It is widely used for classification problems in natural language processing, such as question answering, information extraction, and part-of-speech tagging. Using a maximum entropy approach they extracted features from the corpus, train maximum entropy model with an annotated corpus, and then use it with additional product feature discovery to extract product features from customer reviews. In contrast, the unsupervised product feature extraction approach automatically extracts product features from consumer reviewers without involving training examples. Hu et al, Liu et al. [Liu, 06] proposed a technique based on language pattern mining to identify product features from pros and cons in reviews in the form

3 Feature based Opinion Mining for Customer Reviews 75 of short sentences. They also make an effort to extract implicit features. Their focus is on mining opinion/product features that the reviewers have commented on. Part Of Speech (POS) tag sequence rules were used to extract product attributes, and then the polarities of opinion phrases on the attributes were judged based on the context information. Popescu et al. (2005) developed an unsupervised information extraction system called OPINE, which extracted product features and opinions from reviews. OPINE first extracts noun phrases from reviews and retains those with frequency greater than an experimentally set threshold and then assesses those by OPINE s feature assessor for extracting explicit features. The assessor evaluates a noun phrase by computing a Point-wise Mutual Information score between the phrase and meronymy discriminators associated with the product class. Popescu et al apply manual extraction rules in order to find the opinion words. III. SYSTEM IMPLEMENTATION The system implementation consists of four main modules such as Pre-processing, Text Extraction, Association Mining and Feature Ranking, which are discussed below. The system architecture of the proposed approach is shown in Fig. 1. (A) Pre-processing To start the pre-processing, reviews are collected from review pages by periodically crawling the e-commerce websites such as etc. The data is in HTML format which contains different tags. These review documents are then cleaned to remove tags, after that, extract only text of reviews. Reviews are split into sentences and make a bag of sentences. After extraction reduplicate reviews are removed and the rest of the reviews are stored in the database. To retrieve opinion sentences that are obvious and a user is glad to read. In this work extract explicit features at the sentence level and discard the implicational features. (B) Text Extraction Text Extraction aims to find what people like and dislike about a given product. Therefore how to find out the product features that people talk about is an important step. This paper focus on finding features that appears explicitly as nouns or noun phrases in the reviews. Nouns and consecutive nouns are used as candidate product feature to generate feature set. To identify nouns/ noun phrases from the reviews using the part-ofspeech tagging. This work uses the NLP standford parser, which parses each sentence and yields the part-of-speech tag of each word (whether the word is a noun, verb, adjective, etc) and identifies simple noun and verb groups. Here is an example sentence: Speaker phone quality is good but the price is more. For this sentence, the POS tagging representation is: Figure 1: System Architecture

4 76 G. Vinodhini, L. Srisubha and R. M. Chandrasekaran Each sentence is saved in the review database along with the POS tag information of each word in the sentence. A transaction file is then created for the generation of frequent features in the next step. In this file, each line contains words from a sentence, which includes only preprocessed nouns/noun phrases of the sentence. The reason is that other components of a sentence are unlikely to be product features. (C) Association Mining Association analysis, which is useful for discovering interesting relationships hidden in large datasets the uncovered relationships, can be represented in the form of association rules or sets of frequent items. Let I = {i 1,, i n } be a set of items, and D be a set of transactions (the dataset). Each transaction consists of a subset of items in I.. An association rule is an implication of the form X Y, where X I, Y I, and X Y = Φ. The rule X Y holds in D with confidence C if C% of transactions in D that support X also support Y. The rule has support s in D if S% of transactions in D contains X Y. The problem of mining association rules is to generate all association rules in D that have support and confidence greater than the user specified minimum support and minimum confidence. In order to find the frequent features, association mining is used. In this context, an itemset is a set of words or a phrase that occurs together. The idea behind this technique is that features that appear on many opinions have more chance to be relevant, and therefore, more likely to be actually a real product feature. To mine frequent occurring phrases, each piece of information extracted above is stored in a dataset called a transaction set/file. Then it runs the association rule miner, which is based on the Apriori algorithm. It finds all frequent itemsets in the transaction file. Each resulting frequent itemset is a possible feature. In this work, we define an itemset as frequent if it appears in more than minimum support of the review sentences. The Apriori algorithm works in finds all frequent itemsets from a set of transactions that satisfy a user-specified minimum support. For this task, to find frequent itemsets with three words or fewer in this work believe that a product feature contains no more than three words. The generated frequent itemsets, which are also called candidate frequent features. Frequent Features are stored to the feature set for further processing. This work used a much simpler mechanism and yet very effective. A simple algorithm can count the frequency with which words appear in different opinions, eliminating those less frequent. The end result is a subset of words called frequent features that have great chance of actually being a real feature. Not all frequent features generated by association mining are useful or are genuine features. There are also some uninteresting and redundant ones. Feature pruning aims to remove these incorrect features. Two types of pruning are presented: (a) Compactness pruning checks features that contain at least two words, which are named feature phrases, and removes those that are likely to be meaningless. (b) Redundancy pruning removes redundant features that contain single words. (D) Feature Ranking From the generated frequent feature itemset using apriori algorithm, frequent 2-itemset combinations are processed to eliminate the irrelevant product feature using word cooccurrence method. After finding out the relevant product feature, in-order to list out the mostly discussed product feature term frequency method is used for ranking. IV. RESULTS AND DISCUSSION The performance evaluation is calculated using precision, recall and F-measure. Precision is the fraction of retrieved instances that are relevant, while recall is the fraction of relevant instances that are retrieved. Both precision and recall are therefore based on an understanding and measure of relevance. The two measures are sometimes used together in the F-measure (also F-score) is a measure of a test s accuracy. It considers both the precision and the recall of the test to compute the score asurement for a system. To evaluate the method using precision, recall and F-measure is used to measure the effectiveness of the approach. Experiment is

5 Feature based Opinion Mining for Customer Reviews 77 conducted on the customer reviews of five electronics products: 2 digital cameras, 1 DVD player, 1 IPOD, and 1 mobile phone. Reviews are collected from the two websites namely Amazon.com and Cnet.com. Products in these sites have a large number of reviews. Each of the reviews includes a text review and a title. Additional information available but not used in this project, include date, time, author name. Table I shows the performance value for five different products. Table I Performance Values for five Different Products Product Name Precision Recall F-Measure Mobile phone Digital camera Digital camera IPOD DVD player Average candidate features (noun or consecutive nouns) are product features is subjective. It is common that a candidate feature is a product feature in one product domain, but it is not product feature in the other product domains. The measured values of performance evaluation differs due to method used and domain used. In general, the average recall value is greater than the average precision value. F- Measure is used to show the accuracy. V. CONCLUSION The Product feature extraction is an important task of review mining and summarization. Opinion features are mined from product reviews based on data mining and natural language processing methods. A feature-based summary of a large number of customer reviews of a product sold online is obtained. This problem will become increasingly important as more people are buying and expressing their opinions on the Web. This work tried to find out better product features from customer reviews. The performance evaluation results show that the precision was found to be 0.73, Recall was found to be 0.83 and F-measure was found to be Experimental results indicate that the proposed techniques are effective in performing their tasks. After feature extraction, Opinion words related to feature has to be extracted and then sentiment classification has to be done in future. References Figure 2: Performance Values for Five Different Products From the figure it is evident that the average precision is calculated as 0.73, the average recall is calculated as 0.83 and average F-measure is calculated as The precision is low since it returns any noun as a feature if it often occurs in the documents. For example in phone data set and digital camera data set: phone and digital camera are not product features. Judging what [1] Khairullah Khan, Baharum B. Baharudin, Aurangzeb Khan, and FazalfiefiMalik, Automatic Extraction of Features and Opinion Oriented Sentences from Customer Reviews, World Academy of Science, Engineering and Technology 62, (2010). [2] Gamgarn Somprasertsri, Pattarachai Lalitrojwong, Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization, Journal of Universal Computer Science, 16(6), (2010), [3] Bing Xu, Tie-Jun Zhao (2010), Product Features Mining Based on Conditional Random Fields Model Harbin Institute of Technology. [4] Gamgarn Somprasertsri, Pattarachai Lalitrojwong (2008), A Maximum Entropy Model for Product Feature Extraction in Online Customer Reviews, King Mongkut s Institute of Technology Ladkrabang Bangkok

6 78 G. Vinodhini, L. Srisubha and R. M. Chandrasekaran [5] Popescu, A. M., Etzioni, O.: Extracting Product Features and Opinions from Reviews, In Proc. Conf. Human Language Technology and Empirical Methods in Natural Language Processing, Vancouver, British Columbia, 2005, [6] M. Hu, and B. Liu, Opinion Extraction and Summarization on the Web, AAAI., (2006), [7] Tak-Lam Wong, Wai Lam (2005), Hot Item Mining and Summarization from Multiple Auction Web Sites The Chinese University of Hong Kong, Shatin, Hong Kong.