TextBlob is more of a natural language processing library, but it comes with a rule-based sentiment analysis library. Polarity is a float that lies in the range of [-1,1] where 1 means a positive statement and -1 means a negative statement. Subjective sentences generally refer to personal opinion, emotion, or judgment whereas objective refers to factual information. 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. The compound score is the sum of positive, negative & neutral scores which is then normalized between -1(most extreme negative) and 1 (most extreme positive).
E-commerce has become more popular with the growth in internet and network technologies. Many people feel convenient to buy products online using various forums such as Amazon, Flipchart, Awok etc. When customers buy the products online there is an option for them to provide their review comments. Many customers chose to provide their experience, opinion, feedback etc. Such product reviews are rich in information consisting of feedback shared by users.
Vader stands for Valence Aware Dictionary and sEntiment Reasoner. It is a lexicon and rule based tool for sentiment analysis. It is specifically attuned to sentiments expressed in social media. It is used for analyzing the sentiment of text which contains both positive and negative polarity. The main function of VADER is to quantify how much of positive or negative emotion is present in the text. It can also measure the intensity of emotion.
We have explained how to get a sentiment score for words in Python. Instead of building our own lexicon, we can use a pre-trained one like the VADER which stands from Valence Aware Dictionary and sEntiment Reasoner and is specifically attuned to sentiments expressed in social media. You can install the VADER library using pip like pip install vaderSentiment or you can get it directly from NTLK. You can have a look at VADER documentation. Notice that the pos, neu and neg probabilities add up to 1. Also, the compound score is a very useful metric in case we want a single measure of sentiment.
People around the globe are more actively using social media platform such as Twitter, Facebook, and Instagram etc. They share information, opinions, ideas, experiences and other details in the social media. The business communities have become more aware of these developments and they want to use the available information in their favor. One of the ways to understand the people opinions on the product they are using is by collecting tweets related to those products. Then performing the sentiment analysis on the tweets collected on a particular topic.