Why Twitter is best suited for sentiment analysis and why you need Twitter sentiment analysis?

What is Twitter Sentiment Analysis?

Twitter sentiment analysis is the process of determining the emotional tone behind a series of words. It is a technique used in text mining to analyze the underlying sentiment of text messages (tweets). The Twitter sentiment or opinion expressed through it may be positive, negative or neutral. However, no algorithm can provide you with 100% accuracy or prediction of sentiment analysis.

As part of natural language processing, algorithms such as SVM, Naive Bayesd are used to predict the polarity of sentences. sentiment analysis of Twitter data may also depend on the sentence level and document level.

However, the approach of finding positive and negative words in a sentence is not appropriate because the style of a text block depends heavily on the context. This can be done by looking at POS (Part of Speech) markers.

Why Twitter is best suited for sentiment analysis?

The most popular social media site for extracting information is Twitter. Most of the papers reviewed use Twitter as their social media environment. This is due to the availability, accessibility, and richness of Twitter content. Twitter enables users to post and interact with short messages, thus allowing them to express their opinions. There are millions of tweets per day on almost any topic. The availability of content or data for public use has made Twitter popular and the best social network for sentiment analysis.

User-generated content on Twitter can provide valuable information to academics, business organizations and governments. twitter conducts real-time analysis and keeps a close eye on public sentiment, as Twitter has about 500 million tweets per day and allows the public to access its data through an API. There are Twitter users all over the world, so it is full of opinions and views of people from other countries, languages and ideas.

Why you need Twitter sentiment analysis?

Twitter is full of opinionated tweets, and there are a wide range of applications for sentiment analysis, such as the following.

1. Public opinion monitoring

Since users can express their opinions relatively freely on social media, this makes social media an important way to generate and spread public opinion topics. Through sentiment analysis of social media, it can provide an effective tool for the government to understand public opinion and guide public opinion. For negative news, it can pacify people's emotions in a more timely manner to avoid further deterioration of the situation. At the same time, the government can also develop corresponding strategies to improve the existing services.

2. Event prediction

With the development of the Internet, more and more people are willing to express their views on a certain event on social media. A large part of the Twitter conversation revolves around news and politics. This makes it a great place to gauge public opinion, especially during campaigns.

Twitter sentiment analysis can provide interesting insights into how people feel about a particular candidate (you can even track sentiment over time to see how it evolves).

Twitter has emerged as a popular platform for political discourse. Modern politics occurs when people comment on Twitter or link to campaigns.

In addition, Twitter is often used to predict stocks.

3. Helping users make decisions on buying or not

For example, when consumers are hesitant to buy a product, they will naturally check other people's reviews of the product. If most of the reviews are good, the consumer will probably make a purchase; conversely, if most of the reviews are bad, the consumer will generally not make a purchase. If sentiment analysis can be done for social media like Twitter, which is both highly current and has a wide range of topics, it will be more convenient for users.

4. Help companies conduct market research

After launching a new product, companies can use sentiment analysis to get useful information from a large number of user reviews. For example, what users like, what they don't like, and what positive or negative effects they have on the company's products and services. Thus, companies can understand their own strengths and weaknesses, and can better develop corresponding measures for service improvement, so as to take the initiative in the fierce market competition.