Sentiment Analysis using Python

Introduction:
Sentiment Analysis is the process of ‘computationally’ determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mining, deriving the opinion or attitude of a speaker.

Why sentiment analysis?
●Business: In marketing field companies use it to develop their strategies, to understand customers’ feelings towards products or brand, how people respond to their campaigns or product launches and why consumers don’t buy some products.
●Politics: In the political field, it is used to keep track of political view, to detect consistency and inconsistency between statements and actions at the government level. It can be used to predict election results as well! .
●Public Actions: Sentiment analysis also is used to monitor and analyse social phenomena, for the spotting of potentially dangerous situations and determining the general mood of the blogosphere.

Installation:
Command to install vaderSentiment :
●pip install vaderSentiment

VADER Sentiment Analysis :

VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media. VADER uses a combination of A sentiment lexicon is a list of lexical features (e.g., words) which are generally labelled according to their semantic orientation as either positive or negative. VADER not only tells about the Positivity and Negativity score but also tells us about how positive or negative a sentiment is.

What is Compound Score?
The Compound score is a metric that calculates the sum of all the lexicon ratings which have been normalized between -1(most extreme negative) and +1 (most extreme positive).

● positive sentiment : (compound score greater than= 0.05)
● neutral sentiment : (compound score greater than -0.05) and (compound score less than 0.05)
● negative sentiment : (compound score less than -0.05)

Source Code:
Link - https://drive.google.com/drive/folders/1O7CflD9VI9J9DhHfiighEnvUsfsF787F?usp=sharing

#Sentimentanalysis, #Sentimentanalysispython, #MachineLearning, #SentimentAnalysisInPython
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