Deep learning framework based on Word2Vec and CNNfor users interests classification
Abubakr H. Ombabi, Onsa Lazzez, Wael Ouarda, Adel M. Alimi · 2017
Social media has given internet users a venue for sharing and expressing their interests and opinions on different life sides. Daily, millions of users generate huge volume of reviews and comments on social media which reflect their opinions on different issues. Analyzing these opinions manually is a very hard task. Thus, opinion analysis is the task of computationally analyzing opinions expressed in social data. However, there are few works that have considered both sentiment analysis and classification to determine users' topic of interest. In this study, an approach that combines both sentiment analysis and classification was proposed. The main objective of this work is to design an effective method to provide a summary of users interests from Twitter based on their social textual data on five categories which are sports, travel, fashion, food and religion. Thus we are able to discover the topic in which users are interested. Inspired by the successes of deep learning, our proposed system takes advantages of pre-trained Word2Vec for text pre-processing and to gain vector representations of words which will be the input for suitable Convolutional Neural Network architecture for deep features extraction. Rectified Linear Unit and Dropout functions were applied to improve the accuracy. Support Vector Machine classifier was used to predict the final classification. TensorFlow running on Python 2.7.12 was used to implement our system. This system was tested and validated on different publicly available corpus of reviews and comments from Twitter. The proposed system achieved best accuracy of 97.3% for users interests classification.