Short From AI based Sentiment Analysis

By Oliver D'Esposito

Email: oliverdesposito@gmail.com

What is the ideal AI model for short text sentiment analysis?

Working Overview

Sentiment Analysis

A computer determining the emotional value of a body of text.

A Example

A Example

"was taking pictures in the UK and beautiful this fan walked up to me"

A Example

"was taking pictures in the UK and beautiful this fan walked up to me"

"taking pictures in the UK and horrible this fan walked up to me"

A Example

"was taking pictures in the UK and beautiful this fan walked up to me"

"taking pictures in the UK and horrible this fan walked up to me"

A Example

1.0

0.0

Neural Network

A type of ML which uses interconnected units (mathematical functions) to learn features of data. These units are organized in layers.

Neural Network

Forward Feed Neural Network (FFNN): A network in which data is only moved forward

Forward Feed Neural Network (FFNN): A network in which data is only moved forward

Recurrent Neural Network (RNN): Data can be recalled therefor making it effective for sequential data

Recurrent Neural Network (RNN): Data can be recalled therefor making it effective for sequential data

Convolutional Neural Network (CNN): recognizes patterns

Convolutional Neural Network (CNN): recognizes patterns

Long Short Term Memory (LSTM): A RNN that fixed vanishing gradient

Long Short Term Memory (LSTM): A RNN that fixed vanishing gradient

Gradient Recurrent Neural Network (GRU): A simper and faster version of LSTM

Gradient Recurrent Neural Network (GRU): A simper and faster version of LSTM

Why this topic

5,700

Tweets per second

192,000,000

monetizable daily active users

Social Impact

#BML & #MeToo

Other Uses

  • E-Commerce
  • Ad Campaigns
  • Comments
  • Hate

Applied Project

STEM Thesis

Goal

Collect and analyze Tweets

My process

  • API
  • CRON job
  • Database

How to know the sentiment of a tweet

How to get sentiment


						"#AEWRevolution 😃 -😭 -😢 -😥 -😞 -☹️ -😡"
						"#BBMA ☹️"

Extracting information

  • Tensorflow
  • Numpy
  • MatPlotlib

Lessons learned

  • Backups
  • Tests
  • Docker

Code


class trend_query_genertor():
    def __init__(self, emoji_querys, trends) -> None:
        self.emoji_querys = emoji_querys
        self.trends = trends.__iter__()

    def __iter__(self):
        return self

    def __next__(self):
        query_obj_list = []

        trend_search_term = self.trends.__next__()
        output_objs = []

        for emoji_obj in self.emoji_querys:
            trend_obj = emoji_obj.copy()

            trend_obj['query'] = f"{trend_search_term} {emoji_obj['query']} -is:retweet"            
            trend_obj['trend'] = trend_search_term

            output_objs.append(trend_obj)

        return  output_objs

data conclusions

Word Count

Word Count

37.6 Average

21 Median

Paper conclusions

CNNs are best fitted for short text sentiment analysis

Opposing view

  • Basic models should be used
  • Sequence is needed for Twitter analysis

CNNs are best fitted for short text sentiment analysis

CNNs are best fitted for short text sentiment analysis

Future questions

How would a change in dialects effect my conclusion?

Is sequence more important for cultural nuance?

References

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References

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References

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References

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Thank you

Questions?