How does Sentiment Analysis work?
Sentiment Analysis in NLP, is used to determine the sentiment expressed in a piece of text, such as a review, comment, or social media post.
The goal is to identify whether the expressed sentiment is positive, negative, or neutral. let’s understand the overview in general two steps:
Preprocessing
Starting with collecting the text data that needs to be analysed for sentiment like customer reviews, social media posts, news articles, or any other form of textual content. The collected text is pre-processed to clean and standardize the data with various tasks:
- Removing irrelevant information (e.g., HTML tags, special characters).
- Tokenization: Breaking the text into individual words or tokens.
- Removing stop words (common words like “and,” “the,” etc. that don’t contribute much to sentiment).
- Stemming or Lemmatization: Reducing words to their root form.
Analysis
Text is converted for analysis using techniques like bag-of-words or word embeddings (e.g., Word2Vec, GloVe).Models are then trained with labeled datasets, associating text with sentiments (positive, negative, or neutral).
After training and validation, the model predicts sentiment on new data, assigning labels based on learned patterns.
What is Sentiment Analysis?
Sentiment analysis is a popular task in natural language processing. The goal of sentiment analysis is to classify the text based on the mood or mentality expressed in the text, which can be positive negative, or neutral.
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