Detecting Fraud Reviews of Apps Using Sentiment Analysis
DOI:
https://doi.org/10.26438/ijcse/v7i1.365368Keywords:
Natural Language Processing(NLP), Sentiment Analysis, Sentence Level Categorization, Review Level CategorizationAbstract
Sentiment analysis is one of the main tasks of Natural Language Processing (NLP). This analysis had gained more attention in recent years. In this paper, we tackled the problem of sentiment polarity categorization as one of the fundamental problems of sentiment analysis. A general process is proposed with detailed descriptions. Data used are online product reviews collected from Amazon.com. Experiment for sentence-level categorization and review-level categorization are performed with best outcomes. Finally, we give insight into our future work on sentiment analysis.
References
[1] Kim S-M, Hovy E, Determining the sentiment of opinions In: Proceedings of the 20th international conference on Computational Linguistics, page 1367. Association for Computational Linguistics, Stroudsburg, PA, USA.
[2] Liu B, Sentiment analysis and subjectivity In: Handbook of Natural Language Processing, Second Edition. Taylor and Francis Group, Boca.
[3] Pak A, Paroubek P, Twitter as a corpus for sentiment analysis and opinion mining In: Proceedings of the Seventh conference on International Language Resources and Evaluation.. European Languages Resources Association, Valletta, Malta.
[4] Pang B, Lee L,Opinion mining and sentiment analysis. Found Trends Inf Retr2(1-2).
[5] Twitter, Twitter apis. https://dev.twitter.com/start.
[6] Liu B, The science of detecting fake reviews. http://content26.com/blog/bing-liu-the-science-of-detecting-fake-reviews/.
[7] www.amazon.com.
[8] Go A, Bhayani R, Huang L, Twitter sentiment classification using distant supervision, 1–12. CS224N Project Report, Stanford.
[9] Sarvabhotla K, Pingali P, Varma V Sentiment classification: a lexical similarity based approach for extracting subjectivity in documents. InfRetrieval14 (3): 337–353.
[10] Wilson T, Wiebe J, Hoffmann P, Recognizing contextual polarity in phrase-level sentiment analysis In: Proceedings of the conference on human language technology and empirical methods in natural language processing, 347–354.. Association for Computational Linguistics, Stroudsburg, PA, USA.
[11] Zhang Y, Xiang X, Yin C, Shang L, Parallel sentiment polarity classification method with substring feature reduction In: Trends and Applications in Knowledge Discovery and Data Mining, volume 7867 of Lecture Notes in Computer Science, 121–132.. Springer Berlin Heidelberg, Heidelberg, Germany.
[12] Choi Y, Cardie C, Adapting a polarity lexicon using integer linear programming for domain-specific sentiment classification In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2, EMNLP ’09, 590–598.. Association for Computational Linguistics, Stroudsburg, PA, USA.
[13] Tan LK-W, Na J-C, Theng Y-L, Chang K, Sentence-level sentiment polarity classification using a linguistic approach In: Digital Libraries: For Cultural Heritage, Knowledge Dissemination, and Future Creation, 77–87. Springer, Heidelberg, Germany.
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