People generally use social media sites like Facebook, Instagram, Twitter, and others to share their opinions and feelings. Marketers and companies can learn more about how customers feel about their brand, products, services, etc. by using social media sentiment analysis. Sentiment analysis is a method of applying computational linguistics and Natural Language Processing (NLP) to extract feelings or sentiments from raw text. This article provides you detailed information about the data science for social media sentiment analysis.
What is Social Media Sentiment Analysis?
Social media sentiment analysis is a type of Natural Language Processing (NLP) which uses computational linguistics, machine learning and other data analytics methods to identify and extract subjective information from social media posts or content. This might include the sentiment (positive, negative, or neutral) of a raw text as well as the emotions or opinions that individuals express in it.
Organizations use existing APIs to collect the data like tweets, and then use sentiment analysis tools to understand the people’s feelings about their offerings and products. Organizations can use this to monitor the reputation of their brands and improve their marketing tactics to increase the revenue and sales.
Why Does Social Media Sentiment Analysis Matters?
Social media sentiment analysis can provide organizations with important information about how current and potential consumers view their products and services. Organizations can identify the areas where they are succeeding well and where they need to make improvements by tracking the overall state of a brand, product, or service on social media platforms.
Let us know a few reasons why organizations want to monitor their brand, products, or services on social media.
You may acquire a deeper understanding of your audience by conducting sentiment analysis on social media content. It may help to determine the satisfaction of the clients with their goods and products.
Sentiment analysis can help organizations to gain actionable insights such as what client segment is most successful, which of their services is performing well, and which aspect of the product is not performing better. Businesses might use these insights to improve their marketing strategies or modify their products.
Organizations can interact directly with their customers on social media by using social media sentiment analysis. Publicly responding to complaints or negative feedback and resolving client issues has been a common practice.
Also Read: Data Science or Software Engineering? How to Pick Right Tech Path
Top Sentiment Analysis Project Ideas:
You can consider some of the best project ideas listed below to gain practical experience and build your sentiment analysis model:
Amazon Product Reviews:
Amazon is one of the largest e-commerce companies. Customers buy various products and share their reviews and feedback on the website about their experiences. The goal of this project is to analyze product reviews and to determine which features of the product are working better and which is not.
The dataset for this study can be accessed here.
Analyze IMDb Reviews:
IMDb is another website where users can provide reviews and thoughts about a movie or TV show. To find out what worked and what didn’t in the show or movie, you can perform sentiment analysis on IMDb reviews. The project’s dataset can be downloaded from here.
Rotten Tomatoes Movie Reviews:
Rotten Tomato is a website/platform which allows people and reviewers to write reviews of films and television shows. The goal of this project is to analyze movie reviews and determine how popular a film is by critics and viewers. The project’s dataset can be downloaded from here.
Best Tools of Social Media Sentiment Analysis:
Organizations can implement social media sentiment analysis by using a variety of tools. Among all the tools, the top tools for social sentiment analysis are:
Hootsuite Insights Powered by Brandwatch:
Hootsuite provides this social media monitoring and analysis service. Businesses can use it to track and examine social media activity in real-time on various platforms like Facebook, Instagram, LinkedIn, Twitter etc. Additionally, they have the ability to track specific hashtags and phrases and create alerts to increase notifications.
Digimind:
Businesses may track, analyze, and report on market trends, rivals, and industry developments with the help of Digimind which is a platform for market intelligence and competitive analysis. It gathers data from over 850 million online sources to generate a thorough picture of sentiment towards your business.
Mentionlytics:
Businesses can use this brand and reputation management tool to track and analyze online mentions of their products, services, and brand. Mentionlytics allows customers to monitor brand mentions and online conversations across various social media platforms that includes blogs, LinkedIn, Instagram, Twitter, Facebook, Reddit etc.
Crowd Analyzer:
Crowd Analyzer is a social sentiment analysis tool available in Arabic. This is particularly important for brands with Arabic-speaking target audiences.
TalkWalker:
TalkWalker collects data from more than 150 million web sources. Additionally, the technology analyzes sentiment, tone, and emotions by using artificial intelligence.
Conclusion:
Social Media Sentiment Analysis is known for extracting sentiment or opinions from content uploaded on various social media platforms such as Facebook, Twitter, and others. Businesses can use this analysis to determine which portions of their products are working well and which are working poorly. They can use this to monitor customer opinion of their brand. Sentiment analysis can be implemented in your company by using various tools or by creating your own model.
FAQ’s:
Q1. What is Data Science?
Ans. Data Science is the study of data by using statistical methods, artificial intelligence and some other tools.
Q2. What is Sentiment Analysis?
Ans. Sentiment analysis is the process of applying computational linguistics and Natural Language Processing (NLP) to extract feelings or sentiments from raw text.
Q3. What is the other name of sentiment analysis?
Ans. Sentiment analysis is also known as opinion mining.
Q4. What is the full form of NLP in Artificial Intelligence?
Ans. The full form of NLP is Natural Language Processing.
Q5. What is sentiment analysis in natural language processing?
Ans. In natural language processing, sentiment analysis is a popular task.
Q6. What are the types of sentiment analysis?
Ans. The types of sentiment analysis are:
Fine- Grained sentiment analysis
Emotion detection
Aspect-Based sentiment analysis
Multilingual sentiment analysis.