the process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral.
- Importing Necessary libraries
- setting up the consumer and access api tokens and keys
- Setting up the connection bridge and the search token
- loading the pretrained model
- Data Preprocessing
- Fitting the model
- Plotting The results
- Valence Aware Dictionary for Sentiment Reasoning is a model used for text sentiments analysis that is sensitive to both polarity (positive/negative) and intensity (strength) of emotion.
- It is available in the NLTK package and can be applied directly to unlabeled text data
- Primarily VADER sentiment analysis relies on a dictionary which lexical features to emotion intensities called sentiment scores
- The sentiment score of a text can be obtained by summing up the intensity of each word in the text.
- For example, words like "love", "like", "enjoy", "happy" all convey a positive sentiment
- VADER is intelligent enough to understand basic context of these words, such as "did not love" as a negative sentiment
- It also understands capitalization and punctuation, such as "love!!!"
- Sentiment Analysis on raw text is always challenging however, due to a variety of possible factors:
- Positive and Negative sentiment is the same text data.
- Sarcasm using positive words in a negative way.