Shreya Ganga
ABSTRACT
The internet has turned our lives upside down and has become a global means of communication. As the world is rapidly advancing, many new and challenging calls for humankind are associated. One of those challenges is analyzing the sentiments, i.e. opinions or feelings of the person or any user such as customers, while choosing and buying any product. For cases and situations like this, analysis of sentiments or opinion mining plays a significant role. Sentiment Analysis is vital because the customers can get an overview and understanding of reviews of the customers who have already purchased that particular product. Also, it helps them make decisions about their purchase and hence proceed forward accordingly. In comparison to the existing work, the proposed work considers all the sentiments throughout any conversation or review, whether they are good or bad, and hence classifies them further as positive and negative with their extent i.e. percentages of positivity and negativity in the statement. It also finds out the label of the review or any other conversation so that the users can get an idea about the domain of the conversation. Even though related research work has already been done, there is still a need to improve the accuracy and understandability of sentiment analysis. This work is mainly done by using the zero-shot learning technique. After classifying the reviews and predicting labels, the spaCy model is used with it to get essential keywords and phrases for the conversation. In the proposed work, this is done by discarding greetings with a score greater than 80%.
- C. Chauhan and S. Sehgal, "Sentiment analysis on product reviews," in International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, 2017.Google Scholar
- Solanki, A. & Saxena R. (2019). Text Classification Using Self-Structure Extended Multinomial Naive Bayes. Handbook of Research on Emerging Trends and Applications of Machine Learning, 107-129Google Scholar
- Shivaprasad T K, Jyothi Shetty "Sentiment Analysis of Product Reviews: A Review, International Conference on Inventive Communication and Computational Technologies, (ICICCT), 2017.Google Scholar
- S. C, D. V. Vyas and K. Pradyoth, "Sentiment Analysis Based Product Rating Using Textual Reviews," in International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, 2017.Google Scholar
- Malik, N. and Solanki A (2020). Simulation of Human Brain: Artificial Intelligence-Based Learning. Impact of AI Technologies on Teaching, Learning, and Research in Higher Education, 150-160.Google Scholar
- Tarana Singh, Arun Solanki, Anand Nayyar, “Multilingual Opinion Mining Recommendation System Using RNN for Movie Domain”, International conference in Computing, Communication and Cyber-security (IC4S) 12-13 October, 2019, Chandigarh.Google Scholar
- Anil Singh Parihar, Bhagyanidhi "A Study on Sentiment Analysis of Product Reviews", Department of Information Technology Delhi Technological University in International Conference on Soft-computing and Network Security (ICSNS), 2018.Google Scholar
- Xu, Yun, Xinhui Wu, and Qinxia Wang "Sentiment Analysis of Yelp's Ratings Based on Text Reviews.", 2015.Google Scholar
- Tanjim Ul Haque, Nudrat Nawal Saber, Faisal Muhammad Shah, "Sentiment Analysis" Department of Computer Science & Engineering Ahsanullah University of Science.Google Scholar
- Rain, Callen. "Sentiment Analysis in Amazon Reviews Using Probabilistic Machine Learning" Swarthmore College, 2013.Google Scholar
- Dr V. Govindasamy, Dr M. Ezhilarasan, Dr V. Akila, K. Vadivelan "SENTIMENT ANALYSIS ON PRODUCT REVIEW: A SURVEY", 2019 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), 2019.Google Scholar
- Xing Fang and Justin Zhan "Sentiment Analysis using product data" Department of Computer Science, North Carolina A&T State University, Greensboro, NC, USA, 2015.Google Scholar
- Siddharth Yadav, Tanmoy Chakraborty "IIIT-Delhi, India "Zero-Shot Sentiment Analysis for Code-Mixed Data" The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21).Google Scholar
- Bernardino Romera-Paredes and Philip H. S. Torr "An embarrassingly simple approach to zero-shot learning", The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21).Google Scholar
- Kai Li, Martin Renqiang Min, Yun Fu, "Rethinking Zero-Shot Learning: A Conditional Visual Classification Perspective" Department of Electrical and Computer Engineering, Northeastern University, Boston, USA NEC Laboratories America, Khoury College of Computer Science, Northeastern University, Boston, USA.Google Scholar
- Ziming Zhang and Venkatesh Saligrama "Zero-Shot Learning via Semantic Similarity Embedding" Department of Electrical & Computer Engineering, Boston University, IEEE International Conference on Computer Vision, 2015.Google Scholar
- Kamal Khumar "Text summarization using spaCy- Medium" in Analytics Vidhya (Jul 28, 2020). Available at: https://medium.com/analytics-vidhya/text-summarization-using-spacy-ca4867c6b744 . retrieved : May 12, 2022 at 5.25 a.m.Google Scholar
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