Research Papers On Sentiment Analysis Professional And Personal Goals Essay
The Stanford Sentiment 140 Tweet Corpus  is one of the datasets that has ground truth and is also public available.The corpus contains 1.6 million machine-tagged Twitter messages.Moreover, developers can mix those APIs to create their own applications.Hence, sentiment analysis seems having a strong fundament with the support of massive online data.However, those types of online data have several flaws that potentially hinder the process of sentiment analysis.The first flaw is that since people can freely post their own content, the quality of their opinions cannot be guaranteed.
The document level concerns whether a document, as a whole, expresses negative or positive sentiment, while the sentence level deals with each sentence’s sentiment categorization; The entity and aspect level then targets on what exactly people like or dislike from their opinions.
Figure 2 is a flowchart that depicts our proposed process for categorization as well as the outline of this paper. In Phase 2: 1) An algorithm is proposed and implemented for negation phrases identification; 2) A mathematical approach is proposed for sentiment score computation; 3) A feature vector generation method is presented for sentiment polarity categorization.
In Phase 3: 1) Two sentiment polarity categorization experiments are respectively performed based on sentence level and review level; 2) Performance of three classification models are evaluated and compared based on their experimental results.
The rating is based on a star-scaled system, where the highest rating has 5 stars and the lowest rating has only 1 star (Figure 1).
This paper tackles a fundamental problem of sentiment analysis, namely sentiment polarity categorization [15-21].