A framework for aspect based sentiment analysis on turkish informal texts

The web provides a suitable media for users to share opinions on various topics, including consumer products, events or news. In most of such content, authors express different opinions on different features (i.e., aspects) of the topic. It is a common practice to express a positive opinion on one aspect and a negative opinion on another aspect within the same post. Conventional sentiment analysis methods do not capture such details, rather an overall sentiment score is generated. In aspect based sentiment analysis, the opinions expressed for each aspect are extracted separately. To this aim, basically a two-phased approach is used. The first phase is aspect extraction, which is the detection of words that correspond to aspects of the topic. Once aspects are available, the next phase is to match aspects with the sentiment words in the text. In this work, we present a framework for the aspect based sentiment analysis problem on Turkish informal texts. We particularly emphasize the following contributions: for the first phase, improvements for aspect extraction as an unsupervised method, and for the second phase, enhancements for two cases, extracting implicit aspects and detecting sentiment words whose polarity depends on the aspect. Additionally, we present a tool including the implementations of the proposed algorithms, and a GUI to visualize the analysis results. The experiments are conducted on a collection of Turkish informal texts from an online products forum.