  Aspect-based opinion mining is widely applied to review data to aggregate or
summarize opinions of a product, and the current state-of-the-art is achieved
with Latent Dirichlet Allocation (LDA)-based model. Although social media data
like tweets are laden with opinions, their "dirty" nature (as natural language)
has discouraged researchers from applying LDA-based opinion model for product
review mining. Tweets are often informal, unstructured and lacking labeled data
such as categories and ratings, making it challenging for product opinion
mining. In this paper, we propose an LDA-based opinion model named Twitter
Opinion Topic Model (TOTM) for opinion mining and sentiment analysis. TOTM
leverages hashtags, mentions, emoticons and strong sentiment words that are
present in tweets in its discovery process. It improves opinion prediction by
modeling the target-opinion interaction directly, thus discovering target
specific opinion words, neglected in existing approaches. Moreover, we propose
a new formulation of incorporating sentiment prior information into a topic
model, by utilizing an existing public sentiment lexicon. This is novel in that
it learns and updates with the data. We conduct experiments on 9 million tweets
on electronic products, and demonstrate the improved performance of TOTM in
both quantitative evaluations and qualitative analysis. We show that
aspect-based opinion analysis on massive volume of tweets provides useful
opinions on products.

Computation and Language;Information Retrieval;Machine Learning