Topic-Based Document-Level Sentiment Analysis Using Contextual Cues

Improving Document-Level Sentiment Classification Using Contextual Valence Shifters

Traditional sentiment feature extraction methods in documentlevel sentiment classification either count the frequencies of sentiment words as features, or the frequencies of modified and unmodified instances of each of these words. However, these methods do not represent the sentiment words’ linguistic context efficiently. We propose a novel method and feature set to handle the contextual polar...

Topic Based Sentiment Analysis Using Deep Learning

In this paper , we tackle Sentiment Analysis conditioned on a Topic in Twitter data using Deep Learning . We propose a 2-tier approach : In the first phase we create our own Word Embeddings and see that they do perform better than state-of-the-art embeddings when used with standard classifiers. We then perform inference on these embeddings to learn more about a word with respect to all the topi...

Document Clustering Using Topic Analysis

Topic Sentiment Change Analysis

Public opinions on a topic may change over time. Topic Sentiment change analysis is a new research problem consisting of two main components: (a) mining opinions on a certain topic, and (b) detect significant changes of sentiment of the opinions on the topic and identify possible reasons causing each such change. In this paper, we discuss topic sentiment change analysis using data on the Web. W...

Sentiment Analysis of Document Based on Annotation

I present a tool which tells the quality of document or its usefulness based on annotations. Annotation may include comments, notes, observation, highlights, underline, explanation, question or help etc. comments are used for evaluative purpose while others are used for summarization or for expansion also. Further these comments may be on another annotation. Such annotations are referred as met...