Sentiment analysis is the analysis of opinions or emotions from text data. Sentiment analysis, also called opinion mining. though it is helpful for review of movies, products, customer services etc. It detects the polarity within the text. however, It helps us to decide whether the specific product or service is good or bad or preferred or not preferred.
thus, It is also useful to recognize opinions of people about any event or their emotions. also, it finds contradiction of text whether it is positive, negative or neutral.
How does Sentiment Analysis Work?
In the first process we will train a model to associate a particular input to the corresponding output based on the samples used for training. so, The feature extractor sends the text input into a feature vector
In the second process, the feature extractor is used to transform unobserved text inputs into feature vectors. Then we will feed feature vectors into the model, which will generate predicted tags ( positive, negative, or neutral).
Why Is It Important?
Every day huge amount of text data is created( emails, support tickets, chats, social media conversations, surveys, articles, documents, etc)and it’s hard to analyze, and also time-consuming
therefore, Sentiment analysis helps make sense of unstructured data by automatically understanding and processing it.
Open Source Libraries available:-
- Scikit-learn
- NLTK
- SpaCy
- TensorFlow
- Keras
- PyTorch
Sentiment Analysis Algorithms :
There are following methods for sentiment analysis:
1. Rule Based Approach
It performs the process based on the set of manually crafted rules. It involves following operations:
- Stemming: From Stemming we will process the root form of a word. Root or Stem is the part to which inflectional affixes(like -ed, -ize, etc) are added. We would create the stem words by removing the prefix of the suffix of a word. So, stemming a word may not result in an actual word.
For Example: Mangoes —> Mango
- Lemmatization : As stemming, lemmatization does the same but the only difference is that lemmatization ensures that the root word belongs to the language. Because of the use of lemmatization we will get the valid words. In NLTK(Natural language Toolkit), we use WordLemmatizer to get the lemmas of words.
- Part of Speech tagging: The pos(parts of speech) explain you how a word is used in a sentence. In the sentence, a word has different contexts. The basic natural language processing models like bag-of-words fail to identify these relations between the words. For that we use parts of speech tagging to mark a word to its parts of speech tag based on its context in the data. Pos is also used to extract the relationship between the words.
2. Automatic Sentiment Analysis
In this approach it truly digs into the text and delivers the goods. This type of analysis uses machine learning to figure out the general meaning of the text.
Sentiment Analysis may involve following Classification algorithms :
- Linear Regression
- Support Vector Machine(SVM)
- Naive Bayes
Applications in Sentiment Analysis:-
- Social Media Monitoring: With the help of this Analysis, we can take the reviews about posts ,blogs ,services Etc.
- Customer Feedback: Sentiment Analysis is also used to take the insights of customer feedback.
- Product Analysis: It gives the overview of products like product is good or bad etc.
- Voice of Customer: Sentiment analysis allows you to categorize and structure the data to identify patterns.
- Market Research: With the help of sentiment analysis, marketers can collect data and opinion from different sources, without compromising reliability. Marketers collect feedback from the customers so that they can do improvements if it requires.
Conclusion
In this article we have seen that using sentiment analysis we can analyze the opinion of a person by product reviews, services, social-media reviews ,movie reviews etc.
We have also seen how such analysis works and its methods. thus, this analysis allows us to extract perception, better understand our customers, and empower our own teams more effectively so that they do better and more productive work.Sentiment analysis is an incredible technology.
Written by: Ruchi
Reviewed by: Shivani Yadav
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