Have you ever wondered about the sentiment surrounding a particular topic, brand, or event on Twitter? Today, we will be developing a bot around this subject. Our objective is to create a bot using BotFleet that conducts sentiment analysis on tweets and sends us notifications when it detects highly negative sentiments.

The Plan

Our goal is the following:

1. Monitor tweets for a specific keyword or hashtag.
2. Analyze the sentiment of these tweets.
3. Send an email alert for tweets with a very negative sentiment.

Prepping Your Toolkit

Before we get started, you'll need:

1. A BotFleet account, for deploying our sentiment-savvy bot.
2. Basic Python knowledge, for scripting our bot's logic.
3. Access to the Twitter API, for fetching tweets.
4. The TextBlob library (or any other sentiment analysis library), for evaluating tweet sentiments.
5. An email service capable of sending emails programmatically.

The Script

Below is a simplified Python script that outlines our bot's workflow. This script uses TextBlob for sentiment analysis and assumes you have set up access to the Twitter API and your email server.

import os   
import tweepy
from textblob import TextBlob
from smtplib import SMTP
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart

# Twitter API credentials
consumer_key = os.environ.get('TWITTER_CONSUMER_KEY')
consumer_secret = os.environ.get('TWITTER_CONSUMER_SECRET')
access_token = os.environ.get('TWITTER_ACCESS_TOKEN')
access_token_secret = os.environ.get('TWITTER_ACCESS_TOKEN_SECRET')

# Email setup
sender_email = "[email protected]"
receiver_email = "[email protected]"
password = os.environ.get('EMAIL_PASSWORD')

# The keyword or hashtag to monitor
keyword = "#YourKeywordHere"

def fetch_tweets():
    auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
    auth.set_access_token(access_token, access_token_secret)
    api = tweepy.API(auth)

    tweets = api.search(q=keyword, count=10, lang="en")
    for tweet in tweets:
        analyze_sentiment(tweet.text)

def analyze_sentiment(tweet_text):
    analysis = TextBlob(tweet_text)
    if analysis.sentiment.polarity < -0.5:  # Threshold for very negative sentiment
        send_email_alert(tweet_text, analysis.sentiment.polarity)

def send_email_alert(tweet, sentiment):
    message = MIMEMultipart()
    message["From"] = sender_email
    message["To"] = receiver_email
    message["Subject"] = "Very Negative Tweet Alert!"
    
    body = f"A tweet with very negative sentiment was detected:\n\n\"{tweet}\"\n\nSentiment score: {sentiment}"
    message.attach(MIMEText(body, "plain"))
    
    with SMTP("smtp.example.com", 587) as server:
        server.starttls()
        server.login(sender_email, password)
        server.sendmail(sender_email, receiver_email, message.as_string())
        print("Sent an email alert for a very negative tweet.")

def main():
    fetch_tweets()

What's Happening Here?

Fetching Tweets: We're using the Tweepy library to search for recent tweets that match our specified keyword or hashtag.

Analyzing Sentiment: TextBlob analyzes the sentiment of each tweet, scoring its polarity from -1 (very negative) to 1 (very positive).

Email Alerts: For tweets with a polarity score below -0.5 (our threshold for "very negative"), the script sends an email with the tweet and its sentiment score.

Setting Up the Bot on BotFleet

Head over to BotFleet and go to the bots section. Name it something memorable, like "Sentiment Analysis Bot".

Indicating script

Copy the Python script we just wrote into the script section.

Indicating requirements

We're using the textblob and tweepy libraries. Let's add these to our requirements:

textblob
tweepy

Indicating environment variables

Set TWITTER_CONSUMER_KEY, TWITTER_CONSUMER_SECRET, TWITTER_ACCESS_TOKEN, and TWITTER_ACCESS_TOKEN_SECRET and EMAIL_PASSWORD as environment variables.

TWITTER_CONSUMER_KEY=your-consumer-key
TWITTER_CONSUMER_SECRET=your-consumer-secret
TWITTER_ACCESS_TOKEN=your-access-token
TWITTER_ACCESS_TOKEN_SECRET=your-access-token-secret
EMAIL_PASSWORD=your-email-password

Schedule It

Set your bot to run every hour. You can use the following cron expression to run your bot every hour:

0 * * * *

Going Further

This bot is just the beginning. You can enhance it by:

1. Refining the sentiment analysis with more sophisticated algorithms or libraries.
2. Monitoring multiple keywords or hashtags simultaneously.
3. Integrating with more alert systems, like SMS or push notifications, for real-time alerts.

Wrapping It Up

You've just automated the process of monitoring and analyzing Twitter sentiment! This bot not only saves you from manual monitoring but also keeps you informed about very negative sentiments around your chosen topics.

Got tweaks, questions, or insights from using your Sentiment Analysis Bot? Reach out.