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(BELIEVER-We Believe In You )Sentiment Analysis from Texts and Emoticons Feedback/Comments

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Inspiration

We all know that Sentiment will be always done by Machine learning and Data mining with the help of Python, but we done the Sentiment analysis by using JavaScript and its Package library that is React.

What is New

Sentiment is a Node.js module that uses the AFINN-165 wordlist and Emoji-Sentiment-Ranking to perform Sentiment Analysis on arbitrary blocks of input text. Sentiment provides several things: Performance The ability to append and overwrite word / value pairs from the AFINN wordlist The ability to easily add support for new languages The ability to easily define custom strategies for negation, emphasis, etc. on a per-language basis With the use of Bootstrap-5Alpha version we prepared each component with from the scratch. we use React-router-dom to redirect your page not reload your page.

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We love to share our feelings with you all, as you all know covid-19 is at its peak and we have to Protect ourselves and others around you by knowing the facts and taking appropriate precautions. So Our website will also aware you about covid-19 and how to prevent it. Its our pleasure to share these information's to you all who visited our website.

How it Works

AFFIN AFINN is a list of words rated for valence with an integer between minus five (negative) and plus five (positive). Sentiment analysis is performed by cross-checking the string tokens (words, emojis) with the AFINN list and getting their respective scores. The comparative score is simply: sum of each token / number of tokens. So, for example let's take the following: I love cats, but I am allergic to them. That string results in the following:

{     score: 1,     comparative: 0.1111111111111111,     calculation: [ { allergic: -2 }, { love: 3 } ],     tokens: [         'i',         'love',         'cats',         'but',         'i',         'am',         'allergic',         'to',         'them'     ],     words: [         'allergic',         'love'     ],     positive: [         'love'     ],     negative: [         'allergic'     ] } Returned Objects Score: Score calculated by adding the sentiment values of recognized words. Comparative: Comparative score of the input string. Calculation: An array of words that have a negative or positive valence with their respective AFINN score. Token: All the tokens like words or emojis found in the input string. Words: List of words from input string that were found in AFINN list. Positive: List of positive words in input string that were found in AFINN list. Negative: List of negative words in input string that were found in AFINN list.

In this case, love has a value of 3, allergic has a value of -2, and the remaining tokens are neutral with a value of 0. Because the string has 9 tokens the resulting comparative score looks like: (3 + -2) / 9 = 0.111111111 This approach leaves you with a mid-point of 0 and the upper and lower bounds are constrained to positive and negative 5 respectively (the same as each token!). For example, let's imagine an incredibly "positive" string with 200 tokens and where each token has an AFINN score of 5. Our resulting comparative score would look like this: (max positive score * number of tokens) / number of tokens (5 * 200) / 200 = 5   Tokenization Tokenization works by splitting the lines of input string, then removing the special characters, and finally splitting it using spaces. This is used to get list of words in the string.

Future Aspect

In future aspect, this sentiment analysis will be achieve more in the state of Statistical business and it will gives you an easy advise that you have to make change in your decisions. We love to share our feelings with you all, as you all know covid-19 is at its peak and we have to Protect ourselves and others around you by knowing the facts and taking appropriate precautions.

Sentiment Analysis is a study of people's attitude, opinions, and emotions to classify whether it is positive, negative or neutral.

Thanks Giving

I love to work with you GrizzHacks5 ,it was my pleasure to be a part of GrizzHacks5 and devpost community with MLH. i really enjoyed the sessions which is very benificial for me. and i love to work with a team like yours. thanks again.

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