Tweet classification using Semantic Word-Embedding with Logistic Regression

The paper presents a text classification approach for classifying tweets into two classes: availability/ need, based on the content of the tweets. The approach uses a language model for classification based on word-embedding of fixed length to get the semantic relationship among words. The approach uses logistic regression for actual classification. The logistic regression measures the relationship between the categorical dependent variable (tweet label) and a fixed length words embedding of the tweetcontent(words), by estimating the probabilities of tweets produced by embedding words. The regression function is estimated by maximum likelihood estimation of composition of tweets by these embedding words. The approach produced 84% accurate classification for the two classes on the training set provided for shared task on "Information Retrieval from Microblogs during Disasters (IRMiDis)". as a part of, The 9th meeting of Forum for Information Retrieval Evaluation (FIRE 2017).