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20120140506009

  1. 1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 6, June (2014), pp. 53-61 © IAEME 53 ASPECT BASED SENTIMENT ANALYSIS OF MOVIE REVIEWS Mitisha Vaidya1 , Priyank Thakkar2 Nirma University, Ahmedabad, 382481, Gujarat, India ABSTRACT Aspect based Sentiment Analysis identifies user’s sentiment towards particular aspect of an entity. In aspect based sentiment analysis, aspect and sentiment word extraction and sentiment polarity identification are two important tasks. In this paper, Seeded Aspect and Sentiment (SAS) topic model is extended using part of speech (POS) tagging for aspect and sentiment word extraction. Two approaches of SentiWordNet for sentiment polarity identification are also studied in the paper. Keywords: Aspect, Aspect Extraction, Sentiment Analysis, Sentiwordnet, Topic Modeling. I. INTRODUCTION Aspect based sentiment analysis investigates what precisely individual’s likes or dislikes. Document level and sentence level sentiment analysis would not be able to identify user’s opinion towards particular aspect of an entity. Document level analysis represents general opinion of users towards an entity. Sentence level analysis represents user’s opinion sentence by sentence. So, for reviewing any entity accurately, aspect based sentiment analysis is more preferable. In aspect based sentiment analysis, aspect and sentiment word extraction separates aspects that have been assessed [5]. For instance, in the sentence, “The voice quality of this phone is amazing”, the aspect is “voice quality” of the entity “this phone”. Here, “this phone” does not show the aspect GENERAL, in light of the fact that the assessment is not about the phone in general, but just about its voice quality. On the other hand, the sentence “I love this phone.” assesses the phone all in all, i.e., the GENERAL aspect of the entity “this phone”. Sentiment polarity identification figures out if the opinions on different aspects are positive, negative, or neutral [5]. In the first illustration over, the opinion on the “voice quality” aspect is positive. In the second, the opinion on the aspect GENERAL is also positive. INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 5, Issue 6, June (2014), pp. 53-61 © IAEME: http://www.iaeme.com/IJARET.asp Journal Impact Factor (2014): 7.8273 (Calculated by GISI) www.jifactor.com IJARET © I A E M E
  2. 2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 6, June (2014), pp. 53-61 © IAEME 54 II. RELATED WORK In aspect and sentiment word extraction, mainly three techniques are used.First technique is aspect extraction based on frequent nouns and noun phrases [12]. Second technique is aspect extraction by exploiting opinion and target relations [4] and the third technique is aspect extraction using topic modelling [7]. Aspect extraction using topic modelling as discussed in [7] combined features of the first two techniques. In topic modelling, the synonymous aspects must be grouped into the same class. To address this issue, a different setting was presented in [7], where the user gave some seed words for a few aspect class and the model extracted and grouped aspect terms into class at the same time. This setting was paramount on the grounds that arranging aspects was a subjective task. For different application proposed, different arrangements may be required. Some form of user direction is sought. The principle task focused in [7] was to extract the aspects and group them. Notwithstanding, the models could additionally extract aspect specific sentiment word. In sentiment polarity identification, two primary approaches are used. First technique is Lexicon based approach [4],[9] and second technique is supervised learning approach [3]. In this paper, the Lexicon based approach is used as described in [9]. In [9], SentiWordNet is used to determine aspects’ sentiment polarity. This was done for all the sentences in a review and subsequently for all reviews of a movie. The scores for a particular aspect from all the reviews of a movie were aggregated to obtain an opinionated analysis of that aspect. The sentiment analysis around aspects thus first located an opinionated content about an aspect in a review and then used the SentiWordNet based approach to compute its sentiment polarity. This paper examines two methods of SentiWordNet. First method is “Adjective + Adverb Combine” denoted as SWN(AAC)[9] and the second method is “Adjective + Adverb combine” with “Adverb +Verb combine” denoted as SWN(AAAVC)[9]. III. SAS MODEL [7] WITH POS TAGS Figure 1: SAS model [7] with POS tags
  3. 3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 6, June (2014), pp. 53-61 © IAEME 55 ME-SAS [7] used a maximum entropy method to generate priors for aspect's and sentiment's part-of-speech tag. In this paper Stanford-POS-Tagger[10] is used for the same purpose. As this tagger tags the words in the sentences using Maximum Entropy, proposed model does not require to calculate maximum entropy for part-of-speech tags separately. As shown in Figure 1, ߰ௗ,௦is computed at the same level as in ME-SAS. But passing parameters are hyper parameter ߜ and words generated by part-of-speech tagging denoted as “pos” in this study. The equations used to set priors are same as in SAS-model. The entries in the vocabulary is denoted by ܸଵ…௩, where V is the number of unique non-seed terms. ܳ௟ୀଵ…௖is used to signify‫ܥ‬ seed sets, where each seed set ܳ௟is a group of semantically related terms. T aspects and T aspect specific sentiment models are denoted by ߮௧ୀଵ…் ஺ , ߮௧ୀଵ…் ை respectively. Aspect specific distribution of seeds in the seed set Q௟ is represented by ௧,௟. In this study, it is assumed that a review sentence usually talks about one aspect. A review document dଵ..஽ comprises of Sௗ sentences and each sentence s in Sௗhas Nௗ,௦ words. The sentence s of document d is represented by ܵ݁݊‫ݐ‬௦ ௗ . To distinguish between aspect and sentiment terms, an indicator (switch) variable ‫ݎ‬ௗ,௦,௝ ‫א‬ ሼܽො, ‫݋‬ොሽ for the ݆௧௛ term ofܵ݁݊‫ݐ‬௦ ௗ , ‫ݓ‬ௗ,௦,௝ is used. Further, let߰ௗ,௦mean the distribution of aspects and sentiments in ܵ݁݊‫ݐ‬௦ ௗ . Different priors are calculated from the Equations (1), (2) and (3). This equations are same as used in SAS model [7]. ‫݌‬൫ܼௗ,௦ ൌ ‫ݐ‬หܼ֋ௗ,௦, ܴ֋ௗ,௦, ܹ֋ௗ,௦, ܷ֋ௗ,௦൯ ‫ן‬ ‫ܤ‬൫݊௧,ሾሿ ௢ ൅ ߚை ൯ ‫ܤ‬൫݊௧,ሾሿ֋ௗ,௦ ௢ ൅ ߚை൯ ൈ ‫ܤ‬ ቀ݊௧,ሾሿ ௎,஺ ൅ ߚ஺ ቁ ‫ܤ‬ ቀ݊௧,ሾሿ֋ௗ,௦ ௎,஺ ൅ ߚ஺ቁ ൈ Π௟ୀଵ ௖ ‫ܤ‬ ቀ݊௧,௟,ሾሿ ௌ,஺ ൅ ߛቁ ‫ܤ‬ ቀ݊௧,௟,ሾሿ֋ௗ,௦ ௌ,஺ ൅ ߛቁ ൈ ݊ௗ,௧ ֋ௗ,௦ ௌ௘௡௧ ൅ ߙ ݊ௗ,௧ሺ·ሻ ֋ௗ,௦ ௌ௘௡௧ ൅ ܶߙ ሺ1ሻ ‫݌‬൫‫ݎ‬ௗ,௦,௝ ൌ ‫݋‬ොหܼ֋ௗ,௦, ܴ֋ௗ,௦, ܹ֋ௗ,௦, ܷ֋ௗ,௦, ܼௗ,௦ ൌ ‫,ݐ‬ ܹௗ,௦,௝ ൌ ܹ൯ ‫ן‬ ݊௧,௪֋೏,ೞ,ೕ ௢ ൅ ߚை ݊௧,ሺ·ሻ֋೏,ೞ,ೕ ௢ ൅ |ܸ ‫׫‬ ܷ௟ܳ௟|ߚை ൈ ݊ௗ,௦֋೏,ೞ,ೕ ௢ ൅ ߜ௕ ݊ௗ,௦֋೏,ೞ,ೕ ஺ ൅ ߜ௔ ൅ ݊ௗ,௦֋೏,ೞ,ೕ ௢ ൅ ߜ௕ ሺ2ሻ ‫݌‬൫‫ݎ‬ௗ,௦,௝ ൌ ܽොหܼ֋ௗ,௦, ܴ֋ௗ,௦, ܹ֋ௗ,௦, ܷ֋ௗ,௦, ܼௗ,௦ ൌ ‫,ݐ‬ ܹௗ,௦,௝ ൌ ܹ൯ ‫ן‬ ݊௧,௟,௪֋೏,ೞ,ೕ ௌ,஺ ൅ ߛ ݊௧,௟,ሺ·ሻ֋೏,ೞ,ೕ ௌ,஺ ൅ |ܳ௟|ߛ ൈ ݊௧,௟ ൅ ߚ஺ ݊௧,ሺ·ሻ ൅ ሺܸ ൅ ‫ܥ‬ሻߚ஺ ൈ ݊ௗ,௦֋೏,ೞ,ೕ ஺ ൅ ߜ௕ ݊ௗ,௦֋೏,ೞ,ೕ ஺ ൅ ߜ௔ ൅ ݊ௗ,௦֋೏,ೞ,ೕ ௢ ൅ ߜ௕ ; ܹ ‫א‬ ܳ௟ ݊௧,௪֋೏,ೞ,ೕ ୙,୅ ൅ ߚ஺ ݊௧,ሺ·ሻ ୙,୅ ൅ ሺܸ ൅ ‫ܥ‬ሻߚ஺ ൈ ݊ௗ,௦֋೏,ೞ,ೕ ஺ ൅ ߜ௕ ݊ௗ,௦֋೏,ೞ,ೕ ஺ ൅ ߜ௔ ൅ ݊ௗ,௦֋೏,ೞ,ೕ ௢ ൅ ߜ௕ ; ‫,݈׍‬ ܹ ‫א‬ ܳ௟ ۙ ۖ ۘ ۖ ۗ ሺ where‫ܤ‬ሺ‫ݔ‬Ԧሻ ൌ Π೔సభ ೏೔೘ሺೣሬሬԦሻ Γሺ௫೔ሻ ΓቀΣ೔సభ ೏೔೘ሺೣሬሬԦሻ ௫೔ቁ is the multinomial Beta function. Number of times term v assigned to aspect t as an opinion/sentiment word is denoted as ݊௧,௩ ௢ .Number of times non-seed term v in Vassigned to aspect t as an aspect is signified by ݊௧,௩ ௎,஺ . Number of times seed term v in ܸ௟ assigned to aspect t as an aspect is represented as ݊௧,௟,௩ ௌ,஺ .݊ௗ,௧ ௌ௘௡௧ is the number of sentences in document d that were assigned to aspect t. designate The number of terms inܵ݁݊‫ݐ‬௦ ௗ that were assigned to aspects and
  4. 4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 6, June (2014), pp. 53-61 © IAEME 56 opinions are designated as ݊ௗ,௦ ஺ and ݊ௗ,௦ ை respectively. Number of times any term of seed set ܳ௟ assigned to aspect t is represented as ‫ݒ‬௧,௟. Omission of a latter index denoted by “[]” in the above notation represents the corresponding row vector spanning over the latter index. For example, ݊௧,ሾሿ ௎,஺ ൌ ൣ݊௧,௩ୀଵ ௎,஺ … ݊௧,௩ୀ௏ ௎,஺ ൧ and ሺ·ሻdenotes the marginalized sum over the latter index. Counts excluding assignments of all terms inܵ݁݊‫ݐ‬௦ ௗ is denoted by the subscript ֋ ݀, ‫.ݏ‬ Counts excluding ‫ݓ‬ௗ,௦,௝ is represented by ֋ ݀, ‫,ݏ‬ ݆. Hierarchical sampling is performed in this paper. For each sentence ܼௗ,௦, first, an aspect is sampled using Equation (1). Once the aspect is sampled, ‫ݎ‬ௗ,௦,௝ is computed. In ‫ݎ‬ௗ,௦,௝, the probability of ‫ݓ‬ௗ,௦,௝ being an opinion or sentiment term, ‫݌‬ሺ‫ݎ‬ௗ,௦,௝ ൌ ‫݋‬ොሻis given by Equation (2). However, for ‫݌‬ሺ‫ݎ‬ௗ,௦,௝ ൌ ܽොሻ, there are two cases: (i) the observed term ܹ ൌ ‫ݓ‬ௗ,௦,௝ ‫א‬ ܳ௟ or (ii) does not belong to any seed set, ‫,݈׍‬ ܹ ‫א‬ ܳ௟i.e., w is an non-seed term. These cases are dealt in Equation (3). IV. SentiWordNet After extracting aspect and sentiment words for each sentence in a document, for sentiment polarity identification two approaches are implemented. In SWN(AAC), “Adjective” or “Adjective + Adverb combine” words are extracted from the sentences, which contain aspects. Polarities to these words are assigned by SentiWordNet using following algorithm [9]. Here, scaling factor (sf) for adverb is taken 0.35 as suggested in [9]. Adjective is represented by adj and adverb is represented by adv. Algorithm 1: SWN(AAC) [9] For each sentence, extract adv+adj combines. For each extracted adv+adj combine do: • If adj score=0, ignore it. • If adv is affirmative, then o If score(adj)>0 ݂ௌ஺஺஼(adv,adj)= min(1,score(adj)+sf*score(adv)) o If score(adj)<0 ݂ௌ஺஺஼(adv,adj)= min(1,score(adj)-sf*score(adv)) • If adv is negative, then o If score(adj)>0 ݂ௌ஺஺஼(adv,adj)= max(-1,score(adj)+sf*score(adv)) o If score(adj)<0 ݂ௌ஺஺஼(adv,adj)= max(-1,score(adj)-sf*score(adv)) In SWN(AAAVC), “Adverb + verb” patterns are combined with “Adjective + Adverb”. Here “Adverb + Verb” are multiplied with different weight factors from 0.1 to 1 as suggested in [9]. In this implementation, best result is obtained when weight factor is set to 1.
  5. 5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 6, June (2014), pp. 53-61 © IAEME 57 Algorithm 2: SWN(AAAVC) [9] For each sentence, extract adv+adj and adv+verb combines. 1. For each extracted adv+adj combine do: • If adj score=0, ignore it. • If adv is affirmative, then o If score(adj)>0 ݂ௌ஺஺஼(adv,adj)= min(1,score(adj)+sf*score(adv)) o If score(adj)<0 ݂ௌ஺஺஼(adv,adj)= min(1,score(adj)-sf*score(adv)) • If adv is negative, then o If score(adj)>0 ݂ௌ஺஺஼(adv,adj)= max(-1,score(adj)+sf*score(adv)) o If score(adj)<0 ݂ௌ஺஺஼(adv,adj)= max(-1,score(adj)-sf*score(adv)) 2. For each extracted adv+verb combine do: • If verb score=0, ignore it. • If adv is affirmative, then o If score(verb)>0 ݂ௌ஺௏஼ (adv,verb)= min(1,score(verb)+sf*score(adv)) o If score(verb)<0 ݂ௌ஺௏஼ (adv, verb)= min(1,score(verb)-sf*score(adv)) • If adv is negative, then o If score(verb)>0 ݂ௌ஺௏஼(adv, verb)= max(-1,score(verb)+sf*score(adv)) o If score(verb)<0 ݂ௌ஺௏஼(adv, verb)= max(-1,score(verb)-sf*score(adv)) 3. ݂஺஺஺௏஼(sentence)= f(adv,adj)+1*f(adv,verb) IV. EXPERIMENTAL EVALUATION DataSet In all the experiments carried out, benchmark dataset AC1IMDB [6] is used. For aspect and sentiment word extraction seeds are manually created using different film awards, movie review sites and film magazines. This dataset contains 50,000 movie reviews from www.imdb.com. From that, 25,000 movie reviews are negative and 25,000 movie reviews are positive.
  6. 6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 6, June (2014), pp. 53-61 © IAEME 58 Evaluation Measures Accuracy and f-measure are used to evaluate the performance. Accuracy is defined as the ratio of the correctly identified polarities of reviews divided by total reviews. In this paper, user liking a movie is considered as positive review while user disliking a movie is considered as negative review. In this sense, true positive (TP), false negative (FN), false positive (FP) and true negative (TN) are defined as under [13]. TP: the number of correctly identified positive reviews FN: the number of incorrectly identified of the negative reviews FP: the number of incorrectly identified of the positive reviews TN: the number of correctly identified of the negative reviews Based on the above interpretations precision (‫)݌‬ and recall (‫)ݎ‬ are defined in equations (4) and (5) respectively. ‫݌‬ ൌ ܶܲ ܶܲ ൅ ‫ܲܨ‬ ሺ4ሻ ‫ݎ‬ ൌ ܶܲ ܶܲ ൅ ‫ܰܨ‬ ሺ5ሻ F-measure (F) is used to compare classifier on a single measure and it is represented by the equation (6) ‫ܨ‬ ൌ 2‫ݎ݌‬ ‫݌‬ ൅ ‫ݎ‬ ሺ6ሻ Experimental Methodology, Results and Discussions First, pre-processing of the dataset was done using stop-words excluding negative words i.e. not, isn’t, doesn’t. Words that appeared less than five times in corpus are removed. The seeds for aspects were manually made from various film awards sites, film magazines and film review sites. After pre-processing the dataset, SAS model with pos tags is applied on dataset to extract aspect and aspect specific sentiment words. SWN(AAC) and SWN(AAAVC) schemes are used to assign sentiment scores for sentiment words extracted by SAS model. After identifying scores of the sentiment words assigned to the aspects appearing in the review, final score of the review is computed by aggregating the scores of these sentiment words. If score > 0, review is considered positive else negative. Computed polarity is then matched with actual polarity to compute accuracy and f-measure. Table 1: Comparison of SentiWordNet schemes with computed sentiment polarity Scheme Actual Computed (In Comparison to Actual) SWN(AAC) Positive 25000 21736 Negative 25000 17774 SWN(AAAVC) Positive 25000 23002 Negative 25000 19422 Table 1 represents the total number of correctly identified reviews by two SentiWordNet schemes with actual number of reviews. From this result, it can be seen that SWN(AAAVC) provides better result than SWN(AAC). Table 2 shows correctly classified polarities for both the schemes in terms of percentage.
  7. 7. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 6, June (2014), pp. 53-61 © IAEME 59 Table 2: Percentage of correctly classified polarity by two schemes Scheme Correctly Classified Polarity (%) SWN(AAC) Positive 86.94% Negative 71.10% SWN(AAAVC) Positive 92% Negative 77.69% Table 3: Accuracy and f-measure Scheme Performance Measure Value SWN(AAC) Accuracy 70.02% F-measure 78.89% SWN(AAAVC) Accuracy 84.85% F-measure 84.77% As shown in Table 3, accuracy of 84.85% is achieved for the task of sentiment polarity identification by SWN (AAAVC) schemeof SentiWordNet..Figure 2 depicts the impact of different amount of fraction of verb score (weight factor) on the accuracy for the SWN(AAAVC) scheme. It can be seen that best accuracy is achieved when the weight factor is set to 1. Figure 2: Impact of weight factors on accuracy Using aspect level sentiment analysis, detailed review profile of a movie can be represented. Figure 3 shows review profile of a movie with majority positive reviews while Figure 4 depicts the same for a movie with majority negative reviews.
  8. 8. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 6, June (2014), pp. 53-61 © IAEME 60 Figure 3: Review Profile of a movie with majority positive reviews Figure 4: Review Profile of a movie with majority negative reviews
  9. 9. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 6, June (2014), pp. 53-61 © IAEME 61 V. CONCLUSIONS & FUTURE WORK This paper focuses on identifying polarity/sentiment of reviews about the product/items. To identify the sentiment, first, aspects and sentiment words are extracted using SAS model with POS tagging. Using two schemes of SentiWordNet, sentiment scores of the sentiment words related to the aspects appearing in the review are found. After identifying scores of the sentiment words assigned to the aspects appearing in the review, final score of the review is computed by aggregating the scores of these sentiment words. It is evident from the result that SWN(AAAVC) scheme gives better result than SWN(AAC) scheme. One potential direction for the future work can be the experimentation on other data sets of the same domain as well as different domain than the movie reviews. REFERENCES [1] http://www.tripadvisor.com. [2] SentiWordNet, available at http://www.sentiwordnet.isti.cnr.it. [3] Murthy Ganapathibhotla, South Morgan Street, Bing Liu, and South Morgan Street. Mining opinions in comparative sentences. In International Conference on Compu-tational Linguistics (Coling-2008), 2008. [4] Minqing Hu, Bing Liu, and South Morgan Street. Mining and summarizing customer reviews. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004), 2004. [5] Bing Liu. Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers,May 2012. [6] Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies, pages 142{150, Portland, Oregon, USA, June 2011.Association for Computational Linguistics. [7] Arjun Mukherjee and Bing Liu. Aspect extraction through semi-supervised modeling. In ACL, 2012. [8] Bo Pang, Lillian Lee, Harry Rd, and San Jose. Sentiment classi_cation using machine learning techniques. In Conference on Empirical Methods in Natural LanguageProcessing (EMNLP- 2002), pages 79-86, July 2002. [9] V K Singh, R Piryani, and A Uddin. Sentiment analysis of movie reviews. In IEEE explore, 2013. [10] Kristina Toutanova and Christopher D. Manning. 2000. Enriching the knowledge sources used in a maximum entropy part of-speech tagger. In Joint SIGDAT Conference on Empirical Methods, 2000. [11] Bruce Wiebe and O'Hara. Development and use of a gold-standard data set for subjectivity classification. In Association for Computational Linguistics, 1999. [12] L. Zhang and B. Liu. Identifying noun product features that imply opinions. In ACL (short paper), 2011. [13] J. P. Jiawei Han, MichelineKamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann, 3 Edition, July 2011. [14] Ronak Patel, Priyank Thakkar and K Kotecha, “Enhancing Movie Recommender System”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 5, Issue 1, 2014, pp. 73 - 82, ISSN Print: 0976-6480, ISSN Online: 0976-6499. [15] R. Manickam, D. Boominath and V. Bhuvaneswari, “An Analysis of Data Mining: Past, Present and Future”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, 2012, pp. 1 - 9, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [16] Dr. Jamshed Siddiqui, “An Overview of Opinion Mining Techniques”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 7, 2013, pp. 176 - 182, ISSN Print: 0976-6480, ISSN Online: 0976-6499.

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