Unveiling Sentiments: Exploring Public Views on International Women's & Men's Days with Machine Learning Natural Language Processing.

International Women's Day (IWD) is celebrated annually on March 8th, serving as a global platform to recognize the achievements and contributions of women across various spheres of life. While International Men's Day (IMD) exists on November 19th, it often garners less attention and recognition in comparison. In this article, we delve into the data and explore the underlying factors that contribute to the differential celebration between International Women's Day and International Men's Day, offering insights from a data science perspective.

import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer

# Sample text data containing mentions of International Women's Day and International Men's Day
text_data = [
    "Happy International Women's Day! Let's celebrate the achievements of women around the world.",
    "International Men's Day is an important occasion to highlight men's health and well-being.",
    "I'm excited to participate in events marking International Women's Day.",
    "Why isn't International Men's Day celebrated as widely as International Women's Day?",
    "International Women's Day reminds us of the progress we've made in advancing gender equality.",
    "Let's use International Men's Day as an opportunity to address men's mental health issues."
]

# Initialize the sentiment analyzer
sid = SentimentIntensityAnalyzer()

# Analyze sentiment for each text data
sentiments = {'positive': 0, 'neutral': 0, 'negative': 0}

for sentence in text_data:
    sentiment_score = sid.polarity_scores(sentence)
    if sentiment_score['compound'] >= 0.05:
        sentiments['positive'] += 1
    elif sentiment_score['compound'] <= -0.05:
        sentiments['negative'] += 1
    else:
        sentiments['neutral'] += 1

# Print sentiment analysis results
print("Sentiment Analysis Results:")
for sentiment, count in sentiments.items():
    print(f"{sentiment.capitalize()} Sentiments: {count}")        

How sentiment analysis can be implemented using Python's Natural Language Toolkit (NLTK) library.

This is the output from the sentiment analysis conducted.

Sentiment Analysis Results:
Positive Sentiments: 4
Neutral Sentiments: 1
Negative Sentiments: 1        

Sentiment analysis is like a tool that reads and understands how people feel about something, such as a particular topic or event. It's kind of like taking a big collection of people's opinions and figuring out if they're happy, sad, or somewhere in between.

Imagine we have a bunch of sentences written by different people, some talking about International Women's Day and some about International Men's Day. We want to see how people feel about these special days.

So, we use a special tool called a sentiment analyzer to help us understand the feelings behind these sentences. The analyzer reads each sentence and gives it a score based on how positive, negative, or neutral it sounds.

For example, if someone writes, "I'm so excited for International Women's Day!", the sentiment analyzer might give it a positive score because the person seems happy and enthusiastic.

On the other hand, if someone says, "Why doesn't anyone talk about International Men's Day?", the sentiment analyzer might give it a negative score because the person seems a bit unhappy or frustrated.

After looking at all the sentences, we can count up how many sound positive, how many sound negative, and how many sound neutral.

This helps us get a sense of how people generally feel about International Women's Day and International Men's Day. Are they excited and happy about one day more than the other? Or do they have mixed feelings?

By understanding these feelings, we can learn a lot about what's important to people and how they view these special days.

Reasons why International Women's Day is very popular.

Media Attention and Awareness.

• Data analysis of online mentions, social media trends, and news coverage reveals a substantial disparity in the visibility and recognition of International Women's Day compared to International Men's Day.
• Tools such as Google Trends and tweets can provide insights into the volume of searches and public interest surrounding each commemorative day. Historical Context.

• International Women's Day traces its origins back to the early 20th century, rooted in the movements for women's rights and gender equality.
• The historical struggle for women's suffrage, labor rights, and social justice has elevated the significance of International Women's Day as a symbol of empowerment and solidarity.

Advocacy and Representation.

• The advocacy efforts of women's rights organizations, grassroots movements, and influential figures have played a significant role in amplifying the visibility and impact of International Women's Day.
• Data analytics can track the engagement levels and reach of advocacy campaigns and initiatives aimed at promoting gender equality and women's empowerment. Cultural Significance.

• International Women's Day has evolved into a global cultural phenomenon, marked by diverse celebrations, events, and initiatives aimed at honoring women's achievements and advancing gender equity.
• Analyzing cultural trends and traditions can offer valuable insights into the enduring significance and resonance of International Women's Day across different societies and communities.

The celebration of International Women's Day reflects a profound commitment to advancing gender equality and recognizing the invaluable contributions of women worldwide. While International Men's Day exists as a complementary observance, the data underscores the enduring significance and societal impact of International Women's Day as a catalyst for positive change and empowerment.

As we commemorate this global day of solidarity, let us harness the power of data-driven insights to champion gender equality and create a more inclusive and equitable world for all. This will in turn create as much impact for International Men's Day as well.