Sentiment assessment of text by analyzing linguistic features and contextual valence assignment

Mostafa Al Masum Shaikh, Helmut Prendinger, Mitsuru Ishizuka

研究成果: Article査読

34 被引用数 (Scopus)


Text is not only an important medium to describe facts and events, but also to effectively communicate information about the writer's positive or negative sentiment underlying an opinion, or to express an affective or emotional state, such as happiness, fearfulness, surpriseness, and so on. We consider sentiment assessment and emotion sensing from text as two different problems, whereby sentiment assessment is the task that we want to solve first. Thus, this article presents an approach to sentiment assessment, i.e., the recognition of negative or positive valence of a sentence. For the purpose of sentiment recognition from text, we perform semantic dependency analysis on the semantic verb frames of each sentence, and then apply a set of rules to each dependency relation to calculate the contextual valence of the whole sentence. By employing a domain-independent, rule-based approach our system is able to automatically identify sentence-level sentiment. A linguistic tool called "SenseNet" has been developed to recognize sentiments in text, and to visualize the detected sentiments. We conducted several experiments with a variety of datasets containing data from different domains. The obtained results indicate significant performance gains over existing state-of-the-art approaches.

ジャーナルApplied Artificial Intelligence
出版ステータスPublished - 2008 7月

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 電子工学および電気工学
  • 人工知能


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