1. sentiment extraction.
2. sentiment categorization.
3. subjectivity classification.
4. summarization of opinions.
5. opinion spam detection.

University of Michigan - Ann Arbor

Since single citation count is not convictive enough as a benchmark for scientific performance, a further NLP sentiment analysis could be helpful. Sentiment analysis could identify the emotions (positive, negative, and neutral) within text data so that we can classify all the citations in both articles to figure out the actual opinions.

Sentiment analysis

https://arxiv.org/ftp/arxiv/papers/1601/1601.06971.pdf

Sentiment Analysis of Twitter Data: A Survey of
Techniques


Social networking platform, including Twitter, Facebook, Google+, allow people to view hot topics and newest events and express their attitudes. Given the quantity of the data, the dataset is extremely important for verifying the opinion mining and sentiment analysis algorithms' performance.

Sentiment Analysis of Twitter Data

1. Opinion: A conclusion open to dispute.
2. View: subjective opinion.
3. Belief: deliberate acceptance and intellectual assent.
4. Sentiment: opinion representing one'feelings.

Key Elements of Sentiment Analysis

Sub-tasks of Sentiment Analysis

Detect the opinion holder and opinion orientation at the same time. 

Learn Before

Related