Draft:Sentiment Trend Chart

• Comment: In accordance with Wikipedia's Conflict of interest policy, I disclose that I have a conflict of interest regarding the subject of this article. Shafihafizmalik (talk) 16:30, 3 June 2025 (UTC)

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Overview

The Sentiment Trend Chart typically employs a line graph format, where:

• X-axis: Represents time intervals (e.g., days, weeks, months).
• Y-axis: Indicates sentiment scores or counts, which can be derived from various sentiment analysis methods.

Each line on the chart corresponds to a specific sentiment category, allowing viewers to observe how sentiments fluctuate over time. This visualization aids in identifying patterns, such as spikes in negative sentiment following a particular event or a gradual increase in positive sentiment due to a successful marketing campaign.

Applications

Sentiment Trend Charts are utilized across various domains:

• Social Media Monitoring: Tracking public reaction to events, product launches, or campaigns by analyzing sentiment trends on platforms like Twitter or Facebook.behaveannual.org+1linkedin.com+1
• Customer Feedback Analysis: Assessing customer satisfaction over time through reviews and feedback, helping businesses identify areas of improvement.
• Market Research: Understanding consumer sentiment towards brands or products, which can inform marketing strategies and product development.
• Political Analysis: Monitoring public opinion on policies or political figures, providing insights into voter sentiment and potential election outcomes.

Construction and Methodology

Creating a Sentiment Trend Chart involves several structured steps that combine data science with data visualization techniques:

1. Data Collection

Raw textual data is gathered from sources such as social media platforms (e.g., Twitter, Facebook), customer reviews, surveys, or support tickets. This step forms the foundation of sentiment analysis.

2. Sentiment Analysis

Natural Language Processing (NLP) techniques are applied to classify each text snippet into sentiment categories like positive, negative, or neutral. This can be achieved using:

• Lexicon-based methods (e.g., VADER)
• Machine learning models (e.g., Naïve Bayes, SVM)
• Deep learning (e.g., LSTM, BERT)

3. Aggregation Over Time

The results are aggregated into time intervals (daily, weekly, monthly), allowing for time-series comparison. Sentiment scores or counts are calculated for each interval and category.

4. Visualization Using ChartExpo

To plot sentiment data efficiently and accurately, tools like ChartExpo are used.

ChartExpo is a third-party add-in for Microsoft Excel, Google Sheets, and Power BI that simplifies the process of creating advanced and insightful visualizations without the need for complex coding.

In the context of sentiment trend analysis, ChartExpo:

• Provides pre-built templates for trend line and time series charts.
• Allows users to map multiple sentiment lines (positive, negative, neutral) with color-coding.
• Supports interactivity and customization (e.g., annotations, filters).
• Reduces the time required to transform sentiment data into business-ready dashboards.

This low-code/no-code approach is particularly beneficial for analysts and business users who may lack technical backgrounds but still need to derive insights from sentiment data over time.

Best Practices

When designing a Sentiment Trend Chart:

• Clarity: Ensure that the chart is not cluttered. Limit the number of sentiment categories displayed simultaneously to maintain readability.
• Consistency: Use consistent color schemes and labeling conventions throughout the chart.
• Context: Provide context for significant changes in sentiment trends, such as annotations for events or campaigns that may have influenced public opinion.
• Interactivity: Incorporate interactive elements like tooltips or filters to allow users to delve deeper into specific time periods or sentiment categories.