Several hundreds of thousands of raw data files are uploaded by users every day to social media sites. Online user data provides access to an enormous amount of information regarding products, services, places, and events, which makes it suitable for sentiment analysis. Valuable information can be extracted by analyzing the sentiment of the data.
It is a method for interpreting opinions within a text that uses Natural Language Processing (NLP) to extract positive, negative, and natural meanings from user-generated content shared on social media platforms. Sentiment analysis has been previously applied to products or movie reviews to understand customers’ interests better and, thus, improve outcomes and service offerings. Sentiment Analysis Types/Levels & Methods
Experts define sentiment analysis with three categories/levels, i.e., Sentence level, Document level, and Feature level. This exercise classifies positive and negative sentiments from a sentence, document, or feature.
It has been determined that two main sentiment analysis methods exist: machine-learning techniques and lexicon-based techniques. The machine learning approach focuses on extracting and detecting sentiments from data, while the lexicon-based approach counts positive and negative words that appear in the data.
Social Media/Networking Sites and Types based on Content Sharing
There are many social networking sites for users to share their minds, life stories, narratives, and opinions online. There are four different types of social media sites, based on what types of content users are allowed to share online:
- Content communities (Youtube, Instagram);
- Social networking (Facebook, LinkedIn),
- Blogs (Reddit, Quora);
- Micro-blogging (Twitter, Tumblr).
Microblogging sites are the most common social media sites to extract information for sentiment analysis. These micro-blogging social sites, particularly the likes of Twitter, are found to be the best for gathering insights from user-generated posts and opinions shared online.
The online ecosystem of social media platforms is getting more mature as time goes by. Social media platforms are turning out to become an ocean of valuable information. Each social media content might be different, and it is worth exploring the right social media platform to source or extract users’ information. However, bloggers, WordPress, YouTube, and other social media sources are given much less attention when extracting insights from users’ opinions shared online.
The most popular social media site to extract information is Twitter. Most of the reviewed papers use Twitter as their social media context. This is due to the availability, accessibility, and richness of Twitter content. Twitter enables users to post and interact with short messages allowing them to express opinions. There are millions of tweets every day on almost any topic. The available content or data for public use makes Twitter popular and the best social network for sentiment analysis.
Twitter — Why It’s Best for Sentiment Analysis
The user-generated content on Twitter can provide valuable information to scholars, business organizations, and the government. Twitter conducts real-time analysis and closely public sentiment as Twitter has about 500 million tweets per day, and it allows public access to its data through API. Twitter is used to search and collect tweets from 8 different countries from western and eastern countries. There is Twitter user all around the world, thus making it rich with opinion and views by people from other countries, languages, and perceptions.
Application of Sentiment Analysis
Application context of sentiment analysis The application of sentiment analysis ranges from business and marketing, politic, and health to public action. Sentiment analysis is not limited to one application but provides a vast application in different areas to assist decision-making. Sentiment analysis can be applied to events, activities, sports, or worldwide disasters.
Here are some significant applications of sentiment analysis to look into. Sentiment analysis can:
- raise awareness of data security and the danger of security breaches;
- acts as a guideline for companies to respond to security breaches in shaping public perception;
- Identify people’s sentiment needs during a disaster and prepare an appropriate rescue & response plan
- Help find the level of depression of a person by overserving and analyzing emotions from the text.
Furthermore, sentiment analysis was conducted on social media’s unemployment rate and employment sentiment score. We can see the application of sentiment analysis in healthcare and where the study uses Sentiment analysis as a service framework proposed and utilize Spatio-temporal properties to identify locations of disease outbreaks. Political elections can be predicted with sentiment analysis based on Twitter data. There is a 94% correlation between Twitter data and polling data, which indicates that users’ opinions on the social sharing platform are more reliable than polling data. Twitter may therefore be an even more reliable polling method than sophisticated methods.
Choosing the Right Social Media for Sentiment Analysis
Facebook boasts of being the largest social sharing platform by user numbers. But sentiment analysis through Facebook is not feasible as the data here is too messy to extract. The user-generated content on the platform needs to be structured better.
Users often commit spelling blunders and errors in sentence framing while posting content on Facebook, making the data hard to extract any context. The case is the same with other significant content and social sharing platforms. This puts microblogging/micro-sharing sites in a better place as the best choice for sentiment analysis.
Studies show that 88% of the data comes from Twitter. The other source of social media is not preferable because the number of data or opinions that can be extracted is limited, such as in Blogspot, YouTube, and WordPress.
How Social Media Sentiment Analysis Makes a Business Impact
Businesses gain significant advantages from sentiment analysis by identifying how customers perceive their products or service—assessing the effectiveness and capability of social media and business brand communication and analyzing the flow of businesses and stock prices.
It is displayed by a study comparing sentiment data on Big Six consumer tweets (Britain’s largest and oldest gas and electricity supplier and new entrant energy consumer). The result indicates that the sentiment from the Big six is more negative than a new entrant energy consumer. In addition, sentiment analysis on social media allows the organization to evaluate the success level of a program, as shown in a study where a high positive sentiment is obtained from a tweet on community development program activity. The result can help improve the community’s overall living standard.
There are many applications for sentiment analysis, including improving quality and strategy within a business, forecasting elections, monitoring disease outbreaks, increasing awareness of the importance of data security, improving perception towards a particular sport, and locating and responding to disasters more effectively.
Now that sentiment analysis helps us understand people’s perceptions and make decisions, we have realized how important it is. However, developing a universal sentiment analysis model can be applied to a wide range of data types. Exploring other social networking sites to obtain user opinions and expanding the context of sentiment analysis applications are essential for future recommendations.
This post is originally published at click here