Opinion and Polarity Detection within Far-East Languages in NTCIR-7

This paper presents our work in the Multilingual Opinion Analysis Task (MOAT) done during the NTCIR-7 workshop. This is our first participation in this kind of retrieval and classification task in which we participated for the English, Japanese and traditional Chinese language. As a basic model we suggested a probabilistic model derived from Muller's method [1] that allows us to determine and weight terms (isolated words, bigram of words, noun phrases, etc.) belonging to a given category compared to the rest of the corpus. In the current task, the classification categories are positive, negative, neutral and not opinionated. To succeed at this classification task, we have adopted the logistic regression method in order to define the most probable category for each input sentence. Our participation was strongly motivated by the objective to suggest an approach on the polarity subtask of the MOAT with a minimal linguistic component.