Sentiment Analysis Algorithm

A sentiment analysis algorithm is an algorithm that can solve an sentiment analysis task.

• AKA: Opinion Mining Algorithm.
• Context:
  • It can make use of a set of Opinion Words.
  • It can (typically) be a Classification Algorithm.
  • It can be:
    • a Heuristic-based Sentiment Analysis Algorithm.
    • a Machine Learning-based Sentiment Analysis Algorithm (such as a Supervised Sentiment Analysis Algorithm or an Unsupervised Sentiment Analysis Algorithm).
• See: Information Extraction Algorithm.

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References

2009

• (Ding et al., 2009) ⇒ Xiaowen Ding, Bing Liu, and Lei Zhang. (2009). “Entity Discovery and Assignment for Opinion Mining Applications.” In: Proceedings of ACM SIGKDD Conference (KDD-2009). doi:10.1145/1557019.1557141
• (Jin et al., 2009) ⇒ Wei Jin, Hung Hay Ho, Rohini K Srihari. (2009). “OpinionMiner: A Novel Machine Learning System for Web Opinion Mining and Extraction.” In: Proceedings of ACM SIGKDD Conference (KDD-2009). doi:10.1145/1557019.1557148.

2008

• (Ding et al., 2008) ⇒ Xiaowen Ding, Bing Liu, and Philip S. Yu. (2008). “A Holistic Lexicon-based Approach to Opinion Mining.” In: Proceedings of the International Conference on Web Search and Web Data Mining (WSDM 2008).

2006

• (Esuli & Sebastiani, 2006) ⇒ Andrea Esuli, and Fabrizio Sebastiani. (2006). “SentiWordNet: A Publicly Available Lexical Resource for Opinion Mining.” In: Proceedings of LREC 2006.
• (Choi et al., 2006) ⇒ Yejin Choi, Eric Breck, and Claire Cardie. (2006). “Joint Extraction of Entities and Relations for Opinion Recognition.” In: Proceedings of Empirical Methods in Natural Language Processing (EMNLP 2006).
  • We present an approach for the joint extraction of entities and relations in the context of opinion recognition and analysis. ...

2005

• Yejin Choi, Claire Cardie, Ellen Riloff, and Siddharth Patwardhan. (2005). “Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns.” In: Proceedings of the Human Language Technology Conference/Conference on Empirical Methods in Natural Language Processing (HLT-EMNLP 2005).
• (Popescu & Etzioni, 2005) ⇒ Ana-Maria Popescu, and Oren Etzioni. (2005). “Extracting Product Features and Opinions from Reviews.” In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT-EMNLP 2005).
  • … This paper introduces Opine, an unsupervised information-extraction system which mines reviews in order to build a model of important product features, their evaluation by reviewers, and their relative quality across products....

2004

• (Hu & Liu, 2004) ⇒ Minqing Hu, and Bing Liu . (2004). “Mining and Summarizing Customer Reviews.” In: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004).
  • … Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results.
• Soo-Min Kim, and Eduard Hovy. (2004). “Determining the Sentiment of Opinions.” In: Proceedings of the 20th International Conference on Computational Linguistics (ACL 2004).

2003

• (Dave et al., 2003) ⇒ Kushal Dave, Steve Lawrence, and David M. Pennock. (2003). “Mining the Peanut Gallery: Opinion extraction and semantic classification of product reviews.” In: Proceedings of the 12th International Conference on World Wide Web (WWW 2003). doi:10.1145/775152.775226
  • … Our classifier draws on information retrieval techniques for feature extraction and scoring, and the results for various metrics and heuristics vary depending on the testing situation. The best methods work as well as or better than traditional machine learning. ...

2002

• (Pang et al., 2002) ⇒ Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. (2002). “Thumbs Up?: sentiment classification using machine learning techniques.” In: Proceedings of the ACL-2002 Conference on Empirical Methods in Natural Language Processing
  • … we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) ...
• (Turney, 2002) ⇒ Peter D. Turney. (2002). “Thumbs up or Thumbs Down?: Semantic orientation applied to unsupervised classification of reviews.” In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (ACL 2002).
  • This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs.

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