Track emotional words in emails

Tracking emotional words in emails can be useful for various purposes such as improving customer service, monitoring team communication, or enhancing marketing strategies. Emotional language can provide insights into the tone and sentiment of the email and help in understanding the emotional state of the sender. Here’s how you can go about tracking emotional words in emails:

1. Define Emotional Categories

Start by identifying the categories of emotions you want to track. These can be broadly classified as:

• Positive Emotions: Happy, Excited, Grateful, Hopeful, etc.

• Negative Emotions: Angry, Frustrated, Sad, Disappointed, etc.

• Neutral Emotions: Calm, Neutral, Indifferent, etc.

2. Create a List of Emotional Words

Build a list of words that belong to these categories. Some common examples might include:

• Positive: Amazing, Joyful, Thrilled, Proud, Optimistic, Cheerful, Thankful

• Negative: Furious, Upset, Miserable, Irritated, Hopeless, Stressed, Depressed

• Neutral: Neutral, Normal, Okay, Fine

3. Use Text Analysis Tools

Utilize sentiment analysis or text analysis tools to help detect these emotional words. Some tools can automatically scan the email for keywords and detect sentiment. These tools often come with predefined lists of emotional words, or you can customize them to suit your needs. Examples of text analysis tools include:

• Natural Language Processing (NLP) tools:

  • VADER (Valence Aware Dictionary and sEntiment Reasoner) – This tool is popular for detecting the sentiment of short texts such as emails or tweets.

  • TextBlob – Another Python-based library that can be used to extract sentiment from text.

• Emotion API: Tools like IBM Watson and Microsoft Azure’s Text Analytics API can analyze text and identify emotions like happiness, sadness, anger, etc.

4. Build a Custom Algorithm

If you want more control, you can create a custom script that checks for emotional words in the email body. For example, in Python, you can use libraries such as nltk or spaCy for natural language processing to detect emotion-laden words.

Basic steps for building a Python script:

• Tokenize the email text into words.

• Compare each word with the emotional words list.

• Classify the email based on the balance of emotional words found.

Example Python code using nltk:

python
CopyEdit
import nltk
from nltk.tokenize import word_tokenize

# Example of positive and negative words
positive_words = ['happy', 'excited', 'joyful', 'grateful']
negative_words = ['angry', 'frustrated', 'sad', 'disappointed']

def track_emotions(email_text):
    words = word_tokenize(email_text.lower())  # Tokenize the email text
    positive_count = sum(1 for word in words if word in positive_words)
    negative_count = sum(1 for word in words if word in negative_words)

    if positive_count > negative_count:
        return "Positive Emotion"
    elif negative_count > positive_count:
        return "Negative Emotion"
    else:
        return "Neutral Emotion"

email = "I am so happy and excited about this project!"
print(track_emotions(email))

5. Visualize Emotional Trends

If you’re tracking emotions across multiple emails, you can visualize the results in graphs or charts. Tools like Excel, Google Sheets, or more advanced platforms like Tableau or Power BI can help visualize the sentiment over time, or how customer sentiment changes in response to certain actions.

• Create a sentiment timeline for emails over weeks or months.

• Monitor how emotional tone varies by team or department.

• Assess customer emotions after receiving certain marketing emails or responses.

6. Monitor Email Interactions

• Customer Service: Analyze email responses to identify whether customers are happy, dissatisfied, or upset about a product or service. Positive emotions might indicate satisfaction, while negative emotions may signal issues needing attention.

• Team Communication: Track how team members are communicating. High levels of frustration or negative sentiment might indicate communication breakdowns or workplace stress.

• Marketing Campaigns: Track emotional responses from customers to specific marketing campaigns. Negative emotions might show dissatisfaction, while positive emotions can confirm success.

7. Use it for Feedback and Action

The emotional tone tracked in emails can help improve communication strategies:

• Customer Retention: If customers are frequently expressing frustration, it may highlight issues that need addressing in your service or product.

• Employee Well-being: If internal emails show a lot of negative emotion, it may indicate that employee morale is low, and further investigation or intervention is needed.

• Personalization: In marketing, understanding emotional tones can help tailor communication for specific customer segments.

8. Ethical Considerations

Keep in mind the ethical implications of tracking emotions in emails, especially with regards to privacy and consent. It’s important to clearly inform individuals that their emails may be analyzed, and ensure that sensitive emotional data is handled responsibly.

By leveraging emotional word tracking, you can gain valuable insights into communication dynamics and use that information to improve engagement and responses in both professional and customer-facing contexts.