Subject Detection of Algerian Posts for Opinion Analysis

Abstract

Nowadays, opinion analysis and text classification become interesting tasks in social media studies. Moreover, keyword extraction technology is important research topic and the basis of building corpora, information retrieval, text analysis and text classification...etc. Most studies carry out on the binary problem classification which has historically received more attention in the machine learning community compared to multi-class classification problem. They are often done on academic languages, leaving aside the dialects despite of their increasing use in the social networks. There is very little effort that has been dedicated in the Arabic language especially its dialects such as Algerian dialect, intended for the analysis of opinions. In this work, we aim to realize an innovative and original approach for multi-class classification task and subject detection in the field of marketing. These classes are considered as subjects of the Algerian posts. We collected dataset from Facebook and annotated them with 11 labels. We applied TF-IDF word embedding method to vectorise and extract keywords by giving a weight for each token. In the final stage, our model was trained using input vectors. We applied a deep neural network on our annotated dataset. We achieved a precision of 83%.