Sentiment analysis by deep learning approaches

The vocal modulations, facial expressions and gestures in the visual data, along with textual data, help to analyze the affective domain of the opinion holder in a better way. [...]a combined text, vocal and visual data help to create a more robust and emotion specific sentiment analysis model [3]. The proposed deep sentiment analysis framework includes: a. A Convolutional Neural Network (CNN) based model with max-pooling, and dense layers to process features extracted from sentence level utterances. b. A model for processing transcriptions which is trained with CNN layers. The ML based supervised learning approaches include probabilistic models such as Naive Bayes classifiers [5] or Bayesian classifiers [6]. Because of the sparse nature of the text data, the Support Vector Machines (SVMs) are effectively used for classifying transcription sentiments, both for multi-class and binary class problems. The traditional hand crafted feature extraction methods paved ways to deep learning techniques, additionally, the Recurrent Neural Networks (RNN) and Long Short time Memory (LSTM) could take up the spatial and temporal information directly from the raw data [16].