Using Verbs and Adjectives to Automatically Classify Blog Sentiment Paula Chesley Bruce Vincent and Li Xu Rohini K. Srihari Department of Linguistics Department of Computer Science Center of Excellence for Document University at Buffalo and Engineering Analysis and Recognition (CEDAR)
[email protected] University at Buffalo University at Buffalo {bvincent,lixu}@buffalo.edu
[email protected] Abstract 1. How effectively can classes of verbs in blog posts catego- rize sentiment? This paper presents experiments on subjectivity and po- larity classification of blog posts, making novel use of 2. How can we utilize online lexical resources, such as a linguistic feature, verb class information, and of an Wikipedia’s dictionary, to automatically categorize adjec- online resource, the Wikipedia dictionary. The verb tives expressing polarity? classes we use express objectivity and polarity, i.e., a positive or negative opinion. We derive the polar- 3. What is the best way to propagate up sentiment informa- ity of adjectives from their entries in the online dic- tion from the lexical level to the post level? tionary. Each post from a blog is classified as objec- 4. Are blogs different from other genres expressing senti- tive, subjective-positive, or subjective-negative. Our ap- ment and if so, how? proach is topic- and genre-independent, and our method of determining the polarity of adjectives has an accu- Having manually examined hundredsof blogs, the answer to racy rate of 90.9%. Accuracy rates of two verb classes (4) seems to reside precisely in the diverse rhetorical struc- demonstrating polarity are 89.3% and 91.2%. Initial ture of blog posts and large-scale references to other media.