Sentiment analysis is a process that involves the use of natural language processing, text analysis, and computational linguistics to identify and extract subjective information from text data.
This process involves analyzing text data to determine the emotional tone or sentiment expressed in the text.
Sentiment analysis can be performed on various types of text data, including social media comments, customer reviews, and survey responses.
It is a powerful tool that can be used to gain insights into customer feedback, brand influence, market monitoring, and regulatory compliance.
How Does Sentiment Analysis Works?
Sentiment analysis can be implemented simply using a scored word list, such as AFINN, which assigns scores ranging from -5 to +5 to individual words.
To determine the sentiment score for a piece of text, you can split the text into words and compare them with the word list.
For example, if we take the phrase “I love cats, but I am allergic to them,” we can identify the words “love” and “allergic” in the AFINN list with scores of +3 and -2 respectively.
By adding these scores together, we get a total score of +1, indicating that the sentence is mildly positive.
In addition to simple implementations, there are more complex algorithms used in the industry today that can provide accurate sentiment scores for longer pieces of text.
These algorithms often use a combination of natural language processing (NLP) and machine learning.
Also Read: Swarm Intelligence, an algorithm used in Machine Learning.
Reinforcement learning models are also becoming increasingly popular as they can continue to improve over time.
Types of Algorithms Used in Sentiment Analysis
There are three major types of algorithms used in sentiment analysis: automated systems, rule-based systems, and hybrid systems.
Automated Systems
Automatic approaches to sentiment analysis rely on machine learning models like clustering. Long pieces of text are fed into the classifier, and it returns the results as negative, neutral, or positive.
Rule-based Systems
Unlike automated models, rule-based approaches are dependent on custom rules to classify data. Popular techniques include tokenization, parsing, stemming, and a few others.
You can consider the example we looked at earlier to be a rule-based approach. A good thing about rule-based systems is the ability to customize them. These algorithms can be tailor-made based on context by developing smarter rules.
Hybrid Systems
Hybrid techniques are the most modern, efficient, and widely-used approach for sentiment analysis. Well-designed hybrid systems can provide the benefits of both automatic and rule-based systems.
Hybrid models enjoy the power of machine learning along with the flexibility of customization. An example of a hybrid model would be a self-updating wordlist based on Word2Vec. You can track these wordlists and update them based on your business needs.
Examples of Sentiment Analysis
There are several examples of sentiment analysis in action. One such example is the pet supplies company Chewy, which has thousands of reviews on TrustPilot. Chewy uses sentiment analysis tools to gain insights into their reviews, helping them to better understand the feedback and improve their services accordingly.
Another example is the use of vectorization, a process that transforms tokens into unique numeric arrays that represent different features of the tokens. Vectors are used in NLP to find word similarities, classify text, and perform other NLP operations.
Applications of Sentiment Analysis
Sentiment analysis has several applications in various fields, including marketing, customer service, and politics. Some of the common applications of sentiment analysis include:
1. Social Media Monitoring
Social media monitoring involves tracking and analyzing social media conversations to understand public sentiment towards a particular brand or topic.
This process helps businesses to identify customer issues, track brand reputation, and engage with customers. Apps like TikTok and Facebook also uses Sentiment Analysis.
2. Customer Service
Sentiment analysis can be used to analyze customer feedback and identify areas for improvement in customer service. By analyzing customer feedback, businesses can identify common issues and develop strategies to address them, leading to improved customer satisfaction.
3. Market Research
Sentiment analysis can be used to analyze customer feedback and identify trends in customer preferences and behavior.
This information can be used to develop new products and services that meet customer needs and preferences.
4. Politics
Sentiment analysis can be used to analyze public sentiment towards political candidates and issues.
By analyzing social media conversations, news articles, and other sources, sentiment analysis can provide insights into public opinion and help politicians to develop effective strategies.
Sentiment Analysis With Python
Python is a powerful tool for performing sentiment analysis, a use case of Natural Language Processing (NLP) that helps organizations gain insights into their products and services, and make data-driven decisions. Here are some ways Python can be used for sentiment analysis:
- Text Blob: A Python library for NLP that takes text as input and returns polarity (sentiment) and subjectivity (factual or personal opinion).
- Vader: A rule-based sentiment analyzer trained on social media text that returns a dictionary of sentiment scores.
- Bag of Words Vectorization-based Models: Involves pre-processing training text, creating a Bag of Words using Count or TF-IDF Vectorization, and training a classification model.
- Vectorization: A process that transforms a token into a numeric array to represent various features of a token, used for finding word similarities, classifying text, and performing other NLP operations.
- Writing Sentiment Analysis Results to a CSV File: Python can be used to write sentiment analysis results to a CSV file by importing necessary libraries, setting up authentication, and using csv.writer() function.
Sentiment analysis with Python provides a multitude of options for data scientists and organizations to analyze text data and make data-driven decisions.
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