Guanxing Chen
ABSTRACT
In order to fully extract online users’ product reviews, the deep learning model combining TF-IDF (Term Frequency-Inverse Document Frequency) and LSTM (Long Short-Term Memory) is used to perform sentiment analysis of product reviews. Firstly, it uses Python for data collection. Secondly, applies TF-IDF to eliminate duplicate data and obtain key data, and then converts text into word vectors. Finally, performs sentiment analysis of online reviews by use of LSTM. The group comparison experiments show that the model has good performance with the average precision and average F1 value reaching 0.823 and 0.846 respectively.
- Martin Sundermeyer, Ralf Schlüter, and Hermann Ney. 2012. LSTM neural networks for language modeling. In 13th Annual Conference of the International Speech Communication Association 2012 (INTERSPEECH 2012). Int. Speech Commun. Assoc., Portland, OR, United states, 194–197.https://doi.org/10.21437/interspeech.2012-65Google Scholar
Cross Ref
- Bingwei Liu, Erik Blasch, Yu Chen, Dan Shen, and Genshe Chen. 2013. Scalable sentiment classification for big data analysis using naive bayes classifier. In 2013 IEEE International Conference on Big Data (INTERSPEECH 2012). IEEE Computer Society, Silicon Valley, CA, USA, 99–104. https://doi.org/10.1109/BigData.2013.6691740Google ScholarCross Ref
- Xiao Sun, Jia-qi Ye, and Fu-ji Ren. 2014. Multi-strategy based sina microblog data acquisition for opinion mining. In International Conference on Intelligent Computing (ICIC 2014). Springer Verlag, Taiyuan, China, 551–560. https://doi.org/10.1007/978-3-319-09339-0_56Google ScholarCross Ref
- Inbal Yahav, Onn Shehory, and David Schwartz. 2018. Comments mining with TF-IDF: the inherent bias and its removal. IEEE Trans Knowl Data Eng 31, 3 (mar 2018), 437–450. https://doi.org/10.1109/TKDE.2018.2840127Google ScholarDigital Library
- Yann LeCun 2015. LeNet-5, convolutional neural networks. URL: http://yann. lecun. com/exdb/lenet 20, 5 (2015), 14Google Scholar
- Derek A Pisner and David M Schnyer. 2020. Support vector machine. In Machine learning, Sandra Vieira Andrea Mechelli (Ed.). Academic Press, London, 101–121. https://doi.org/10.1016/B978-0-12-815739-8.00006-7Google ScholarCross Ref
Index Terms
- Research on Sentiment Analysis of Online Product Reviews Based on Deep Learning
Recommendations
Aspect based fine-grained sentiment analysis for online reviews
AbstractFine-grained sentiment analysis for online reviews plays more and more important role in many applications. The key techniques here are how to efficiently extract multi-grained aspects, identify associated opinions, and classify ...
Sentiment Analysis for Product Features in Chinese Reviews Based on Semantic Association
AICI '09: Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence - Volume 03Often there are more than one topic, more than one holder and more than one sentiment in an opinion sentence. But currently the studies on sentiment analysis are unable to identify exactly which sentiments a holder expresses on a given topic, so these ...
Sentiment Analysis of Product Reviews Based on JST Model
CSAE '20: Proceedings of the 4th International Conference on Computer Science and Application EngineeringProduct reviews are information that users comment after purchasing products online, and it contains user's sentiment information about the product. Considering that the e-commerce platform implements personal recommendation of products based on ...
Comments