SENTIMENT ANALYSIS OF SOCIAL MEDIA DATA.

What is Sentiment Analysis?

Sentiment analysis is a kind of data mining where you measure the inclination of people’s opinions by using NLP (natural language processing), text analysis, and computational linguistics. We perform sentiment analysis mostly on public reviews, social media platforms, and similar sites.

Following are the main types of sentiment analysis:

Fine-grained

Fine-grained sentiment analysis gives precise results to what the public opinion is about the subject. It classified its results in different categories such as: Very Negative, Negative, Neutral, Positive, Very Positive.

Detecting Emotion

This kind of sentiment analysis identifies emotions such as anger, happiness, sadness, and others. Many times, you’ll use lexicons to recognize emotions. However, lexicons have drawbacks too, and in those cases, you’d need to use ML ALGORITHMS.

Based on Aspect

In aspect-based sentiment analysis, you look at the aspect of the thing people are talking about. Suppose you have reviews of a smartphone, you might want to see what the people are talking about its battery life or its screen size.

Multilingual

Sometimes organizations need to analyze the text of different languages. This form of sentiment analysis is considerably challenging and requires a lot of effort because you’d need many resources.

Eg:

Twitter boasts 330 million monthly active users, which allows businesses to reach a broad audience and connect with customers without intermediaries. On the downside, there’s so much information that it’s hard for brands to quickly detect negative social mentions that could harm their business.

That’s why sentiment analysis, which involves monitoring emotions in conversations on social media platforms, has become a key strategy in social media marketing.

Listening to how customers feel on Twitter allows companies to understand their audience, keep on top of what’s being said about their brand, and their competitors, and discover new trends in the industry.

Input Example:

{"sentenceList": [
"I like double cheese pizza",
"I love black coffee and donuts",
"I don't want to have diabetes"]}

Output Example:

[{
"positive": 0.455,
"negative": 0,
"sentence": "I like double cheese pizza",
"neutral": 0.545,
"compound": 0.3612
},
{
"positive": 0.512,
"negative": 0,
"sentence": "I love black coffee and donuts",
"neutral": 0.488,
"compound": 0.6369
},
{
"positive": 0,
"negative": 0.234,
"sentence": "I don't want to have diabetes",
"neutral": 0.766,
"compound": -0.0572
}]

This Blog is written by :

1. Haroon Ansari
2. Abhishek Tiwari
3. Noemaan Zariwala