what is sentiment analysis in r

Sentiment analysis means contextual data mining wherein you input a sentence, and it is categorized according to the underlying consumer emotions.

A sentiment analysis system for text analysis combines natural language processing (NLP) and machine learning techniques to assign weighted sentiment scores to the entities, topics, themes, and categories within a sentence or phrase.

When we look at any sentence, the human brain searches for sentiment-bearing phrases – that is, words and phrases that carry a tone or opinion and tries to interpret it, usually as adjective-noun combinations. We also draw from our previous experiences and accumulated knowledge to identify each sentiment-bearing phrase and interpret their negativity or positivity.

This is precisely how computer sentiment analysis works. It involves deep learning and machine learning techniques that “trains” the system to instinctively recognize nouns and phrases as “offensive” and categorize them accordingly.

2. What are the steps of Sentiment Analysis?

Sentiment Analysis steps is complex that consist of 5 different procedures to analyze sentiment data. These steps are:

1. Data collection: The first step of sentiment analysis consists of collecting data from user-generated content in blogs, forums, and social networks. These data are disorganized and expressed differently by using various vocabularies, slang, writing context, etc. Manual analysis is almost impossible. Therefore, text analytics and natural language processing are used to extract and classify such data.

2. Text preparation: It consists of cleaning the extracted data before analysis. The Non-textual contents that are irrelevant for the analysis are identified and eliminated;

3. Sentiment detection: The extracted sentences of the reviews and opinions are analyzed. The sentences with subjective expressions (opinions, beliefs, and views) are retained, and sentences with objective communication (facts, factual information) are discarded;

4. Sentiment classification: In this step, subjective sentences are classified in positive, negative, good, bad; like-dislike, but classification can be made by using multiple points;

5. Presentation of output: The main objective of sentiment analysis is to convert unstructured text into meaningful information. When the analysis is finished, the results are displayed on graphs like pie charts, bar charts, and line graphs. Moreover, time can also be analyzed and graphically displayed, constructing a sentiment timeline with the chosen value (frequency, percentages, and averages) over time.

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