Types of sentiment analysis

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Sentiment analysis mainly focus on polarity, feelings and emotions, urgency also on intentions.
  • ·         Polarity (positive, negative, neutral)
  • ·         Feelings and Emotions (angry, happy, sad etc.)
  • ·         Urgency (urgent, not urgent)
  • ·         Intentions (interested, not interested)

Depending on how you want to interpret customer feedback and queries, you can define and tailor your categories to meet your sentiment analysis needs.

here are some of the popular types of sentiment analysis:

1)Fine-grained sentiment analysis

Fine-grained sentiment analysis of social media with emotion sensing. There  are polarity categories available such as :

  • Very positive
  • Positive
  • Neutral
  • Negative
  • Very negative

This is usually referred to as fine-grained sentiment analysis

2)Emotion Detection

Emotion Detection and Recognition from text is a recent field of research that is closely related to Sentiment Analysis. Sentiment Analysis aims to detect positive, neutral, or negative feelings from text, whereas Emotion Analysis aims to detect and recognize types of feelings through the expression of texts, such as anger, disgust, fear, happiness, sadness, and surprise. 



Many emotion detection systems use lexicons or complex machine learning algorithms. One of the downsides of using lexicons is that people express emotions in different ways.

3)Aspect-based sentiment analysis

The big difference between sentiment analysis and aspect-based sentiment analysis is that the former only detects the sentiment of an overall text, while the latter analyzes each text to identify various aspects and determine the corresponding sentiment for each one.

Here’s a breakdown of what aspect-based sentiment analysis can extract:

  • Sentiments: positive or negative opinions about a particular aspect.
  • Aspects: the thing or topic that is being talked about.

 

for example in this text: "The battery life of this camera is too short", an aspect-based classifier would be able to determine that the sentence expresses a negative opinion about the feature battery life.

4)Multilingual Sentiment analysis

An RNN-Based Framework for Limited Data. Word embedding sentiment lexicons, and even annotated data are language specific.

Multilingual sentiment analysis involves a lot of preprocessing and resources. Some resources are available online such as sentiment lexicons. While others need to be created e.g. translated corpora or noise detection algorithms.

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