Sentiment analysis refers to the processing of natural language, computational linguistics and text analysis for identifying the subjective information. Specifically it aims at determining the overall polarity or contextual of the document. It is the Processing of data which targets the variety of sentiments, which includes neutral, positive or negative. This data also focuses on the subjectivity (Subjective Vs. Objective) and the emotional states.
Tips and practices:
1. Learning based Vs. Lexicon based techniques: Entity level sentiment analysis is used in Lexicon based techniques. The technique uses semantic orientation of words (strength and polarity) for scoring the document. This method gives low recall, but high precision. In case of learning based techniques, firstly data set is gathered for neutral, positive and negative classes. Secondly, Features and words are extracted and then they are put in the algorithm. Which method is to be used for business solutions heavily depends upon the language, domain and application.
2. Syntactic Vs. Statistical technique: Syntactic techniques provide better accuracy and they use a syntactic rule of English language which detects verbs, nouns and adjectives. This technique majorly depends the language of the document. Whereas, statistical technique possesses probabilistic background which exactly focuses between words and its categories. This technique has two advantages over the syntactic one: It can be used with other languages with almost no adaptions and for the good results we can import original dataset for machine translation.
These tips and techniques will help you gather important data for sentiment analysis of your brand.
Date: September 25, 2018