Sentiment Analysis using Machine Learning

Arupriya_SenArupriya_Sen Member Posts: 21 Contributor II
edited July 8 in Help
I am doing Sentiment Analysis of Twitter data and I want to use Machine Learning Algorithm to generate the Sentiments or the polarity of the tweets instead of using the Aylien Analyze Sentiment Operator to yield the same. 
1)I have, hence, added a "Sentiment" column in the excel file where I've stored the data after fetching it from Twitter. But this column is automatically getting removed after I use the Process Documents from Data operator. I can't understand why.
2) I can't understand how to analyse the sentiment using any Machine Learning Algorithm after the pre processing of data. 
Please the find the .rmp process and the dataset attached below. Can anyone please help me find a solution to my problem?  :(  Urgent help is needed.


  • Cohen3NCohen3N Member Posts: 2 Contributor I
    To build a deep-learning model for sentiment analysis, we first have to represent our sentences in a vector space. They represent a sentence either by a bag-of-words, which is a list of the words that appear in the sentence with their frequencies, or by a term frequency inverse document frequency (tf-idf) vector where the word frequencies in our sentences are weighted with their frequencies in the entire corpus.
  • Cohen3NCohen3N Member Posts: 2 Contributor I
    edited July 9
    I hope my answer helped you, if you need further assistance then please contact me on the following address- itunes login
  • Arupriya_SenArupriya_Sen Member Posts: 21 Contributor II
    @Cohen3N can you help me with the TF_IDF approach? how do I compare the words in my tweets to the words of the entire corpus??
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