Sentiment Analysis using Topic-Document Embeddings
Madalina Mitroi, Ciprian‐Octavian Truică, Elena‐Simona Apostol, Adina Magda Florea · 2020
Sentiment analysis plays an important role in automatically finding the polarity and insights of users with regards to a specific subject, events, and entity. In this article, we propose a new topic-document embedding (TOPICDOC2VEC) for detecting the polarity of a text. The TOPICDOC2VEC is constructed as the concatenation between a document embedding (DOC2VEC) and a topic embedding (TOPIC2VEC). A DOC2VEC is computed using word embeddings weighted by TF-IDF, while a TOPIC2VEC is computed using the word embeddings for the relevant terms belonging to the topic. To determine the quality of classification using the new proposed topic-document embeddings, we apply two algorithms, i.e., Logistic Regression and Long Short-Term Memory, for the task of sentiment analysis. We build the TOPICDOC2VEC using five word embedding models, i.e., Word2Vec with CBOW and SkipGram, FastText with CBOW and Skip-Gram, and GloVe. The classification task is evaluated using accuracy, precision, and recall. For our empirical validation, we use a large labeled corpus containing game reviews. The experimental results prove that the TOPICDOC2VEC embeddings outperform the DOC2VEC embeddings for detecting the polarity of a document.