Opiniometer

[abdulrcs]

---

slug: opiniometer
date: 04-Mar-2021
summary: Analyze an opinion on a specific topic based on Twitter posts!
techStack: React, Python, Chart.js
category: Personal Project
githubLink: https://github.com/abdulrcs/Opiniometer
image: https://github.com/abdulrcs/abdulrahman.id/assets/54136956/bbe7f444-095a-4683-b609-93684e119f99

Overview

Analyze whether an opinion on a specific topic is Positive / Negative / Neutral based on recent tweets! It's possible using the Natural Language Processing (NLP) concept called Sentiment Analysis that can determine if a chunk of text is positive, negative, or neutral based on its polarity.

Project Goals

I learned about Sentiment Analysis from my Linear Algebra professor at college and was inspired to combine those with my frontend engineering skills by creating a dynamic visualization with Chart.js.

Tech Stack

• React: Used React for the front end with the use of React Hooks for state management and lifecycle, React Router that makes it possible to navigate between components and create a Single Web Application.
• Framer Motion: A Motion system library that makes it smooth and fluid when transitioning between pages.
• Chart.js A data visualization library for displaying the final result.
• Python (Tweepy, TextBlob, Flask): Utilize Python for the Backend, which uses Tweepy to interact with the Twitter API, TextBlob to calculate the polarity of each text, and Flask as a RESTful API that serves all the results in a JSON to communicate in a Frontend.

Features ✨

• Users can search any topic on Twitter
• It will show a chart on how many tweets are positive/negative/neutral etc
• The user can see the actual tweets!

Demo Video

After the API changes in Twitter, the live site no longer works, so here's a demo video I recorded back in 2021.

m2-res_416p.mp4

Design Process

I designed the site first before writing any code to decide on the colors, components, etc to make sure everything is consistent.

Then, I created each React component based on the Figma I made, and added a Tweet component, that I sliced based on the real Tweet component on Twitter, to handle the Check Tweets feature.

After that, I created the API endpoint with Python to analyze the tweets, while making sure to filter retweets and links because most of them are spam.

@app.route('/api/<string:s>', methods=['GET'])
def analysis(s):
  searchTerm = str(s)
  data = {}
  auth = tweepy.OAuthHandler(consumer_key=consumerKey, consumer_secret=consumerSecret)
  auth.set_access_token(accessToken, accessTokenSecret)
  api = tweepy.API(auth)
  quantity = 25
  tweets = tweepy.Cursor(api.search, q=searchTerm+" -filter:retweets -filter:links", lang="en",tweet_mode="extended").items(quantity)

  wpositive = 0
  positive = 0
  spositive = 0
  wnegative = 0
  negative = 0
  snegative = 0
  neutral = 0
  avgPolarity = 0
  data["tweets"] = []
  data["polarity"] = {}
  for tweet in tweets:
    analysis = TextBlob(tweet.full_text)
    polarity = analysis.sentiment.polarity
    avgPolarity += polarity
    if (polarity == 0):
      neutral+=1
      data["tweets"].append([str(tweet.full_text), "Neutral"])
    elif (polarity > 0 and polarity <= 0.3):
      wpositive+=1
      data["tweets"].append([str(tweet.full_text),"Weakly Positive"])
    elif (polarity > 0.3 and polarity <= 0.6):
      positive+=1
      data["tweets"].append([str(tweet.full_text), "Positive"])
    elif (polarity > 0.6 and polarity <= 1):
      spositive+=1
      data["tweets"].append([str(tweet.full_text),"Strongly Positive"])
    elif (polarity > -0.3 and polarity <= 0):
      wnegative+=1
      data["tweets"].append([str(tweet.full_text), "Weakly Negative"])
    elif (polarity > -0.6 and polarity <= -0.3):
      negative+=1
      data["tweets"].append([str(tweet.full_text), "Negative"])
    elif (polarity > -1 and polarity <= -0.6):
      snegative+=1
      data["tweets"].append([str(tweet.full_text), "Strongly Negative"])

  data["polarity"]["spositive"] = spositive
  data["polarity"]["positive"] = positive
  data["polarity"]["wpositive"] = wpositive
  data["polarity"]["snegative"] = snegative
  data["polarity"]["negative"] = negative
  data["polarity"]["wnegative"] = wnegative
  data["polarity"]["neutral"] = neutral

  return jsonify(data)

Learnings and Takeaway

Building Opiniometer was a lot of fun, it started from seeing a research project from my professor to making it live on a real site, this made me realize you can get an idea from anywhere, and implement it yourself while also adding your unique skill and personality into it!