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Various Techniques for Sentiment Analysis of Products Review – A Review
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 144 - Number 8 |
| Year of Publication: 2016 |
| Authors: Anjani Chanji, Rekha Bhatia |
10.5120/ijca2016910422
|
Anjani Chanji, Rekha Bhatia . Various Techniques for Sentiment Analysis of Products Review – A Review. International Journal of Computer Applications. 144, 8 ( Jun 2016), 29-32. DOI=10.5120/ijca2016910422
Abstract
The sentiment analysis is the analysis of the sentiment in the numeric form from the given text input. The text data obtained from the social networks primarily undergoes the emotion mining method to analyze the sentiment of the user messages posted online. The best known sentiment analysis approaches are supervised approaches and utilizes the dictionary with pre-enlisted sentiment weights to evaluate the overall weight of the sentence sentiment. The pre-enlisted sentiment weights are computed in the linguistic dictionaries such as WordNet by Princeton University, SenticNet by MIT andHindi SentiWordnet by IIT Bombay. The appropriate dictionaries areutilized to evaluate the sentiment of the input text data for the social threads, product review, opinion mining, etc.This work focuses upon the detailed literature study over the sentiment analysis and concludes the shortcomings of the existing models to improve the design of the sentiment analysis models
References
- Che, Wanxiang, Yanyan Zhao, Honglei Guo, Zhong Su, and Ting Liu. "Sentence Compression for Aspect-Based Sentiment Analysis." Audio, Speech, and Language Processing, IEEE/ACM Transactions on 23, no. 12 (2015): 2111-2124.
- Liu, Siyuan, Xiaoyin Cheng, and Fan Li. "TASC: Topic-Adaptive Sentiment Classification on Dynamic Tweets." (2015).
- Esuli, Andrea, and Fabrizio Sebastiani. "Sentiwordnet: A publicly available lexical resource for opinion mining." In Proceedings of LREC, vol. 6, pp. 417-422. 2006.
- Kim, Soo-Min, and Eduard Hovy. "Automatic identification of pro and con reasons in online reviews." In Proceedings of the COLING/ACL on Main conference poster sessions, pp. 483-490. Association for Computational Linguistics, 2006.
- Hu, Minqing, and Bing Liu. "Mining and summarizing customer reviews." InProceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 168-177. ACM, 2004.
- Jin, Jian, Ping Ji, and Ying Liu. "Translating online customer opinions into engineering characteristics in QFD: A probabilistic language analysis approach." Engineering Applications of Artificial Intelligence 41 (2015): 115-127
- Suanmali, Ladda, Naomie Salim, and Mohammed Salem Binwahlan. "Fuzzy logic based method for improving text summarization." arXiv preprint arXiv:0906.4690 (2009).
- Ghorashi, Seyed Hamid, Roliana Ibrahim, Shirin Noekhah, and Niloufar Salehi Dastjerdi. "A frequent pattern mining algorithm for feature extraction of customer reviews." In IJCSI International Journal of Computer Science Issues. 2012.
- Yazhini, R., and Raja P. Vishnu. "Automatic summarizer for mobile devices using sentence ranking measure." In Recent Trends in Information Technology (ICRTIT), 2014 International Conference on, pp. 1-6. IEEE, 2014.
- Zha, Zheng-Jun, Jianxing Yu, Jinhui Tang, Meng Wang, and Tat-Seng Chua. "Product aspect ranking and its applications." Knowledge and Data Engineering, IEEE Transactions on 26, no. 5 (2014): 1211-1224.
- Jin, Jian, Ping Ji, and Ying Liu. "Prioritising engineering characteristics based on customer online reviews for quality function deployment." Journal of Engineering Design 25, no. 7-9 (2014): 303-324.
- Kherwa, Pooja, Anish Sachdeva, Dhruv Mahajan, Nishtha Pande, and Praveen Kumar Singh. "An approach towards comprehensive sentimental data analysis and opinion mining." In Advance Computing Conference (IACC), 2014 IEEE International, pp. 606-612. IEEE, 2014.
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