A Simple Approach to Multilingual Polarity Classification in Twitter
Table 5: Performance on multilingual sentiment analysis (not challenges). B4MSA was restricted to use only the multilingual set of parameters.
['language', '[EMPTY]', '[ITALIC] F1', '( [ITALIC] Fpos1+ [ITALIC] Fneg1)/2', 'acc']
According to author’s reports, all tweets were manually labeled by native speakers as pos, neg, or neu. The Arabic dataset contains 2,000 items; the other datasets contain from 58 thousand tweets to more than 157 thousand tweets. We were able to fetch a fraction of the original datasets; so, we drop the necessary items to hold the original class-population ratio. The ratio of tweets in our training dataset, respect to the original dataset, is indicated beside the name. As before, we evaluate our algorithms through a 10-fold cross validation. Both, on the technical side, both papers use fine tuned classifiers plus a variety of pre-processing techniques to prove their claims. Our approach achieves those performance’s levels since it optimizes a set of parameters carefully selected to work on a variety of languages and being robust to informal writing. The latter problem is not properly tackled in many cases.