Automatic Identification of Sentiment Vocabulary: Exploiting Low Association with Known Sentiment Terms

By Michael Gamon, Anthony Aue. 2005

123 citations recorded by the source corpus.

We describe an extension to the technique for the automatic identification and labeling of sentiment terms described in Turney (2002) and Turney and Littman (2002). Their basic assumption is that sentiment terms of similar orientation tend to co-occur at the document level. We add a second assumption, namely that sentiment terms of opposite orientation tend not to co-occur at the sentence level. This additional assumption allows us to identify sentiment-bearing terms very reliably. We then use these newly identified terms in various scenarios for the sentiment classification of sentences. We show that our approach outperforms Turney's original approach. Combining our approach with a Naive Bayes bootstrapping method yields a further small improvement of classifier performance. We finally compare our results to precision and recall figures that can be obtained on the same data set with labeled data.

Subfields: Artificial Intelligence

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lillian Lee
2. Bo Pang
3. Songbo Tan
4. Xueqi Cheng
5. Michael Gamon
6. Bing Liu
7. Mike Thelwall
8. Osmar R Zaiane

Citations in the corpus

• Opinion Mining and Sentiment Analysis (2008, background): “WordNet-defined relations, or other related words (and, along the same lines, opposite labels can be given based on similar information) [12, 20, 89, 90, 131, 146, 148, 155, 289, 299, 300].”
• Pulse: Mining Customer Opinions from Free Text (2005, methodology): “We have begun experimenting with a variation of a technique for bootstrapping from seed words with known orientation [1,2] with promising initial results [15].”
• Current State of Text Sentiment Analysis from Opinion to Emotion Mining (2017, methodology): “” The first works belong to Hatzivassiloglou and McKeown [1997], Hatzivassiloglou and Wiebe [2000], Pang et al. [2002], and Yu and Hatzivassiloglou [2003].”
• A survey on sentiment detection of reviews (2009, methodology): “In addition, Gamon and Aue (2005) described an extension to the technique for the automatic identification and labeling of sentiment terms described in Turney and Littman (2003).”