Note: I don't know the techniques used by Microsoft Live/Bing (9/28/2007), but Google has a paper. To see the model, please check out (Hu and Liu, KDD-2004) and (Liu et al, WWW-2005) below, or the books above (better). Try search for a camera and click on reviews. You will see summarized user opinions on product features/aspects in a bar chart.
Interesting Piece from New Republic:
If you want to be a successful novelist, should you be sentimental in your writing or not?
Recent Keynote and Invited Talks (not updated) (Older Talks)
Invited Talk. “Sentiment Analysis with Lifelong Learning.” ETS, December 7, 2015.
Invited Talk. “Sentiment Analysis with Lifelong Learning.” Brigham Young University, December. 3, 2015.
Keynote speech. “Sentiment Analysis, Lifelong Learning and Intelligent Personal Assistants.” The 2015 Conf. on Technologies and Applications of Artificial Intelligence (TAAI-2015). Taiwan, Nov. 20-22, 2015.
Invited talk. “Sentiment analysis and lifelong machine learning.” Frontiers in Computational Mathematics: AMS Central Fall Sectional Meeting, October 2-4, 2015.
Keynote speech. “The State of Sentiment.” Sentiment Analysis Symposium, New York City, July 15-16, 2015.
Invited tutorial. "Sentiment analysis: mining opinions, sentiments, and emotions." Sentiment Analysis Symposium, New York City, July 15-16, 2015.
Keynote speech. “Deception Detection via Pattern Mining of Web Usage Behavior” Workshop on Data mining For Big Data: Applications, Challenges & Perspectives, Morocco, March 25, 2015
Keynote speech. “Social Media Analysis via Continuous Learning.” Adobe Text Analytics Summit, Feb 26, 2015.
This work is in the area of sentiment analysis and opinion mining from social media, e.g., reviews, forum discussions, and blogs. In our KDD-2004 paper, we proposed the Feature-Based Opinion Mining model,
which is now also called Aspect-Based Opinion Mining
(as the term feature here can confuse with the term feature used in
machine learning). The output of such opinion mining is a
feature-based opinion summary
or aspect-based opinion summary. The commonly known sentiment classification is a sub-task. Our current work is in two main areas,
which reflect two kinds of opinions (or evaluations)
Mining regular (or direct) opinions. Ex: (1). This camera is great. (2). After taking the drug, I got stomach pain.
Mining comparative opinions. Ex: Coke tastes better than Pepsi.
2. Sentiment Analysis or Mining of Regular Opinions
In this research, we aim to mine and to summarize online opinions in reviews,
tweets, blogs, forum discussions, etc. Specifically, we mine features or aspects of entities (e.g., products) or topics on which people have expressed their opinions and determine whether the opinions are positive or negative. For opinion summarization, we advocate the quantitative aspect and the target of opinions because 50% of the people say something is bad is not the same as 5% say it is bad.
Try Search for the Best Restaurant based on specific aspects, e.g., "best burger," "friendliest service." The system is a demo, which uses the lexicon (also phrases) and grammatical analysis for opinion mining.
Amazon Product Review Data (more than 5.8 million reviews) used in (Jindal and Liu, WWW-2007, WSDM-2008; Lim et al, CIKM-2010; Jindal, Liu and Lim, CIKM-2010; Mukherjee et al. WWW-2011; Mukherjee, Liu and Glance, WWW-2012) for opinion spam (fake review) detection. You can also use it for sentiment analysis. It has information about reviewers, review texts, ratings,
product info, etc. Due to the large file size, you may need to use Download Accelerator Plus (DAP) to download. If you use this data, please cite (Jindal and Liu, WSDM-2008).
Pros and cons dataset used in (Ganapathibhotla and Liu, Coling-2008) for determining context (aspect) dependent sentiment words, which are then applied to sentiment analysis of comparative sentiences (comparative sentence dataset). The same form of Pros and Cons data was also used in (Liu, Hu and Cheng, WWW-2005).
Hao Wang, Bing Liu, Chaozhuo Li, Yan Yang, and Tianrui Li. “Learning with Noisy Labels for Sentence-level Sentiment Classification.” to appear in Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP-2019, short paper), Nov 3-7, 2019, Hong Kong, China.
Guangyi Lv, Shuai Wang, Bing Liu, Enhong Chen, and Kun Zhang. Sentiment Classification by Leveraging the Shared Knowledge from a Sequence of Domains. Proceedings of the 24th International Conference on Database Systems for Advanced Applications (DASFAA-2019), April 22-25, 2019.
Shuai Wang, Guangyi Lv, Sahisnu Mazumder, Geli Fei, and Bing Liu. Lifelong Learning Memory Networks for Aspect Sentiment Classification. Proceedings of 2018 IEEE International Conference on Big Data (IEEE BigData 2018), Seattle, December 10-13, 2018.
Zhiyuan Chen, Nianzu Ma and Bing Liu. Lifelong Learning for Sentiment Classification. Proceedings of the 53st Annual Meeting of the Association for Computational Linguistics (ACL-2015, short paper), 26-31, July 2015, Beijing, China.
Zhiyuan Chen, Arjun Mukherjee, Bing Liu, Meichun Hsu, Malu Castellanos, and Riddhiman Ghosh. Exploiting Domain Knowledge in Aspect Extraction. Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2013), October 18-21, 2013, Seattle, USA.
Zhiyuan Chen, Bing Liu, Meichun Hsu, Malu Castellanos, and Riddhiman Ghosh. Identifying Intention Posts in Discussion Forums. Proceedings of The 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT-2013), June 9-15, 2013, Atlanta, USA.
Lei Zhang and Bing Liu. "Extracting Resource Terms for Sentiment Analysis," Proceedings of the 5th International Joint Conference on Natural Language Processing (IJCNLP-2011), November 8-13, 2011, Chiang Mai, Thailand.
Minqing Hu and Bing Liu. "Mining and summarizing customer reviews."Proceedings of the ACM SIGKDD International Conference on
Knowledge Discovery & Data Mining (KDD-2004, full paper), Seattle,
Washington, USA, Aug 22-25, 2004.
Huayi Li, Geli Fei, Shuai Wang, Bing Liu, Weixiang Shao, Arjun Mukherjee and Jidong Shao. Bimodal Distribution and Co-Bursting in Review Spam Detection. Proceedings of International World Wide Web Conference (WWW-2017), April 3-7, 2017, Perth, Australia.
Jing Wang, Clement. T. Yu, Philip S. Yu, Bing Liu, Weiyi Meng. “Diversionary comments under blog posts." Accepted. ACM Transactions on the Web (TWEB), 2015.
Huayi Li, Zhiyuan Chen, Arjun Mukherjee, Bing Liu and Jidong Shao. "Analyzing and Detecting Opinion Spam on a Large-scale Dataset via Temporal and Spatial Patterns." Short paper at ICWSM-2015, 2015.
Arjun Mukherjee, Abhinav Kumar, Bing Liu, Junhui Wang, Meichun Hsu, Malu Castellanos, and Riddhiman Ghosh. Spotting Opinion Spammers using Behavioral Footprints. Proceedings of SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2013), August 11-14 2013 in Chicago, USA.
Arjun Mukherjee, Vivek Venkataraman, Bing Liu, and Natalie Glance. What Yelp Fake Review Filter Might Be Doing. Proceedings of The International AAAI Conference on Weblogs and Social Media (ICWSM-2013), July 8-10, 2013, Boston, USA.
Nitin Jindal and Bing Liu. "Opinion Spam and Analysis."Proceedings of First ACM International Conference on Web Search and Data Mining (WSDM-2008), Feb 11-12, 2008, Stanford University, Stanford, California, USA.