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In this digital era, we are generating 2.5 quintillion bytes of data every day. Thanks to the innovations and advancements in deep learning, the capabilities of algorithms to analyze the text has improved significantly. Use of creative artificial intelligence techniques such as sentiment analysis can be a highly useful tool for in-depth research.
Sentiment analysis has become a vital tool to analyze the data and make sense out of it. Many B2C companies are using Sentiment analysis to get critical insights which can help them to improve customer experience.
What is the Sentiment Analysis?
Sentiment Analysis is also known as Opinion Mining. It comes under the umbrella of Natural Language Processing. In opinion mining, systems are built to identify and extract opinions from the given set of text.
Sentiment Analysis can be applied on review websites, comments on blogs, and social media platform where a large number of text-based opinions are available.
Apart from identifying the opinions, systems are also capable of extracting the attributes of expressions such as:
a) Subject: What is the subject/topic a person is talking about?
b) Opinion holder: Who is the person, or entity that expressed the opinion?
c) Polarity: Whether the expression given by opinion holder was positive or negative?
Sentiment Analysis Algorithms
When it comes to implementing sentiment analysis in real-life, there are multiple methods and algorithms. Sentiment analysis system can be categorized as:
a) The rule-based approach performs analysis based on manually crafted rules.
b) The automatic approach relies on machine learning techniques.
c) The hybrid approach combines rule-based, and machine learning approaches.
This approach defines a set of rules using a scripting language. Then these rules are used to determine the subject, opinion holder, and polarity. The rules use inputs such as:
a) Classic NLP techniques such as stemming, tokenization, part of speech tagging, and parsing.
b) Lexicons - Lists of words and expressions
Approach: Define two lists of words. 1 list containing negative words such as evil, ugly. Another list containing positive words such as good, beautiful
Rule: for every given text:
1. Count the number of positive words.
2. Count the number of negative words.
Results: If the number of positive word appearances is higher, sentence is positive. If the number of negative word appearances is higher, then it is a negative sentence. Otherwise, return neutral.
Automated methods rely on machine learning techniques. In this approach, sentiment analysis is modeled as a classification problem. The classifier is fed with a text, and it returns the corresponding polarity category. Those categories can be positive, negative, or neutral.
The hybrid approach combines rule-based system and machine learning techniques to identify weighted sentiment phrases within their broader context. The hybrid approach is applied on the entire function stack, including tokenization, syntax analysis, and levels of sentiment analysis.
Sentiment Analysis - Use Cases and Applications
80% of the world’s data is unstructured. Most of this data comes from support tickets, emails, articles, and social media. Analysis of these sources of data in the traditional way is complicated, time-consuming, and expensive. Companies use sentiment analysis to gain insights into what their customers feel.
“People will forget what you said, people will forget what you did, but people will never forget how you made them feel.” - Maya Angelou
Sentiment Analysis may give a 360-degree view on how a brand, or product, is viewed by customers. Sentiment analysis can be performed on product reviews and social media engagement to reveal hidden insights about brand and products. An example can include to measure the impact of a new product or to categorize followers’ reaction to the latest brand updates on social media. Private companies like Unamo offers such kind of services.
As customer service is becoming more automated through Machine Learning, understanding the sentiment of a given case becomes critical. Using sentiment analysis, customer service agents auto-sort user email into “urgent” or “not urgent” buckets. Then urgent emails are given highest priority and solved first. It can be highly useful for broadband providers and web-hosting platforms.
The intent analysis identifies a person’s intention behind a message and relates it with an opinion, complaint, suggestion, query, or appreciation. It detects what people want to do with the text rather than what people say with the text.
Sentiment Analysis – Uber Brand Case Study
As Uber operates in 500+ cities worldwide, it gets significant feedback, suggestions, and complaints from its massive user base. Facebook and Twitter are preferred platform among users. The huge amount of data needs to be categorized and analyzed. A team of experts analyzed online conversations about ride cancellation, payments, price, safety, and service. Let’s have a more in-depth look.
They took the latest comments from Uber’s Facebook page, tweets sent to uber and news articles mentioning Uber. The data included 34,173 Facebook comments, 21,603 tweets, and 4,245 news articles. After running a contextual semantic search algorithm on Facebook comment, the following results were achieved.
The sentiment analysis of 34,173 Facebook comments suggests that in all five categories most of the comments were negative except the price. Positive comments were highest about the costs and highest number of negative comments were on services. Thus, we can conclude that most Uber users are happy about Uber prices but dissatisfied with quality of service.
Want to try Sentiment Analysis?
Do you want to try running a real sentiment analysis without doing any programming? You can use ParallelDots Sentiment Analysis API and perform a sentiment analysis on any sentence in multiple languages. This API is accurate and robust against tricky English sentences which contain double negatives such as “not bad” and word order such as “crushed my hopes” and “crush on her.”
Analyze human emotions expressed via text
This was an overview of sentiment analysis algorithms and their applications in data science. When there is a large number of audience, it is not efficient and recommended to analyze customer emotions using humans. We need systems which can understand the nuances of human expressions in text and human emotions.
Sentiment analysis gives us the ability to analyse and gather insights from comments, tweets, emails and other text based communication and help us to understand customer pain-points. This makes decision making process more intelligent and helps us to improve customer experience.
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