What is Sentiment Analysis and its model

 Sentiment analysis is the operation of detecting positive or negative sentiment in text. It’s frequently used by businesses to descry sentiment in social data, gauge brand character, and understand clients.

Types of Sentiment Analysis

Sentiment analysis focuses on the contrariety of a text( positive, negative, neutral) but it also goes beyond opposition to descry specific passions and feelings( angry, happy, sad, etc), urgency( critical, not critical) and indeed intentions.

Graded Sentiment Analysis

However, you might consider expanding your polarity orders to include different situations of positive and negative. If polarity accuracy is important to your business.

Aspect- based Sentiment Analysis

Generally, when assaying sentiments of texts you ’ll want to know which individualized aspects or features people are mentioning in a positive, neutral, or negative way.

That is where aspect- grounded sentiment analysis can help, for illustration in this product review” The battery life of this camera is too short”, an aspect- grounded classifier would be suitable to determine that the judgment expresses a negative opinion about the battery life of the product in question.

Multilingual sentiment analysis

Multilingual sentiment analysis can be delicate. It involves a lot of preprocessing and resources. utmost of these resources are available online(e.g. sentiment dictionaries), while others need to be created(e.g. restated corpora or noise discovery algorithms), but you ’ll need to know how to decode to use them.

Alternately, you could descry language in texts automatically with a language classifier, also train a custom sentiment analysis model to assort texts in the language of your choice.

Types of Sentiment Analysis Model

1. Rule or Lexicon based approach

This approach relies on manually drafted rules for data classification to determine sentiment. This approach use wordbooks of words with positive or negative values to denote their opposition and sentiment strength to calculate a score. Further functionality can also be added by including expressions. Rule based sentiment analysis algorithms can be customized grounded on environment by developing indeed smarter rules.

Disadvantages

The strike of this approach is that it doesn’t take into account how the words are combined in a sentence, it only looks at circumstances.

It’s quick to apply but the model involves a long- term cost disbursement as it requires regular maintenance so that you get consistent and bettered results.

2. Automated or Machine Learning approach

Rather of easily defined rules, this sentiment analysis model uses machine learning to figure out the substance of the statement. This ensures that the perfection of the analysis improves and information can be reused on numerous criteria without it being too complicated. This way involves the use of machine learning algorithms under supervision. An algorithm is trained with numerous sample passages until it can forecast with delicacy the sentiment of the text. Also large pieces of text are fed into the classifier and it predicts the sentiment as negative, neutral or positive.

3. Hybrid approach

Hybrid sentiment analysis models are the most ultramodern, effective, and extensively- used approach for sentiment analysis. supplied you have well- designed hybrid systems, you can actually get the perks of both automatic and rule- based systems. Hybrid models can offer the power of machine learning coupled with the elasticity of customization. The approach that works for your business

A lexicon- based system may work for you supplied you have a good lexicon to calculate on. Still, in numerous cases, especially for analytics related to social media, wordbooks may not adequately serve the purpose. They may not be acclimatized to the language features of evolving language as seen in social media platforms like Twitter and Instagram. Concluding for a hybrid approach with a combination of either wordbook or rule- based approach and machine learning approach could work stylish for you.

We hope this article was insightful and helped you to understand about sentiment analysis. Thank you for showing interest in this blog .If you have any queries related to the No-Code AI platform, please send us email at info@futureanalytica.com.