9-Step Guide to Sentiment Analysis On Social Media

Nov 5

Social media acts as the pulse of public opinion. As users flock to sharing platforms to voice their thoughts and feelings, the treasure trove of data becomes available, offering a unique lens through which to view societal trends, consumer behaviours and more. However, diving into this sea of information requires a strategic approach and the right tools to transform raw data into meaningful insights. This guide will light the path to effective sentiment analysis, so you can navigate the complexities of social media with confidence. Let’s begin.

Step 1: Define Your Research Objectives

Before jumping in, it’s essential to have clear research objectives. What do you want to achieve with this analysis? Defining your goals will guide your entire process, from data collection to tool selection and result interpretation.

Tips for Setting Objectives:

Be Specific: Rather than a vague goal like “understand brand perception,” try for something more focused, like “analyse public sentiment towards sustainable packaging.”
Know Your Metrics: Decide what you’re measuring. Are you looking for changes in positive or negative sentiment, shifts over time, or a deeper understanding of nuanced emotions like anger or joy?

Step 2: Choose the Right Social Media Platforms

Not all social media platforms are equal when it comes to sentiment analysis. Each platform has its own tone, demographics, and style, so choosing the right ones to analyse is crucial. For example, Twitter’s brief format might lend itself to immediate reactions, while longer posts on Facebook or LinkedIn could provide more detailed opinions.

Tips for Selecting Platforms:

Match Platform to Purpose: If your research is about real-time reactions, Twitter may be ideal. For business-focused research, LinkedIn could be more relevant.
Consider Multiple Platforms: Collect data from more than one platform if it will enrich your findings. This diversity can give a fuller view of sentiment trends.

Step 3: Collect Your Data Thoughtfully

Once you’ve defined your objectives and chosen your platforms, it’s time to gather data. This stage is foundational, as the quality of your data will directly impact your results. Data collection tools allow you to pull large sets of social media posts based on keywords, hashtags, or other filters.

Tips for Effective Data Collection:

Use Keywords and Hashtags: Choose terms and hashtags relevant to your topic. For instance, if studying attitudes towards renewable energy, terms like “#GreenEnergy” or “#ClimateAction” could be useful.
Set Time Frames: Specify a time range for your data collection. If you’re studying reactions to a specific event, collect data from a few days before and after the event for a more comprehensive view.
Filter for Quality: Many data collection tools offer filters for location, language, or verified accounts. Use these to refine your data, ensuring that it aligns with your research goals.

Step 4: Select a Sentiment Analysis Tool as Your Guide

Now comes the core of your analysis: choosing a sentiment analysis tool. The ideal tool will do more than just process data—it will guide you through interpretation, helping you make sense of complex sentiments in nuanced social media language. Opt for a tool that supports customisations, as sentiment can vary greatly based on context.

Tips for Tool Selection:

Look for Contextual Capabilities: Tools that map words in networks or clusters can reveal relationships and deeper insights beyond basic positive or negative labels. Look for platforms that offer a network approach to sentiment.
Choose a Tool with Multilingual Capabilities: If your research spans diverse language groups, make sure the tool can analyse multiple languages or dialects.
Test for Customisation: The best tools allow you to customise the analysis, adjusting sentiment definitions to suit your topic. This flexibility is invaluable in tailoring results to your research questions.

Step 5: Process Your Data and Analyse Sentiment

Once your data is collected and loaded into the tool, it’s time for the analysis phase. This is where the tool processes your data to identify the sentiment—positive, negative, or neutral—and any underlying emotions expressed.

Tips for Effective Data Processing:

Validate Your Data: Double-check the initial results for accuracy, especially if your subject matter includes slang, sarcasm, or regional phrases. Correcting initial errors will improve the tool’s performance on the larger dataset. Try re-generating your results until the model becomes stable.

Step 6: Analyse Results and Identify Patterns

After processing, it’s time move onto the results. This stage involves looking at the sentiment distribution and identifying patterns or shifts in sentiment over time. Your tool should make it easy to visualise and interpret these trends.

Tips for Interpreting Results:

Look Beyond the Basics: Beyond just positive or negative sentiment, explore emotions like trust, anger, or surprise if your tool supports it. This can add valuable layers to your analysis, helping you understand how people feel, not just what they feel.
Spot Anomalies: Unusual spikes or drops in sentiment could indicate critical moments for further study, such as a major event or announcement.

Step 7: Validate Your Findings with Contextual Analysis

A major challenge in sentiment analysis is that social media language can be ambiguous. Verifying your findings through a contextual analysis will improve the accuracy and reliability of your results. This means looking at representative posts to confirm that the sentiment aligns with your expectations.

Tips for Contextual Verification:

Sample and Review: Select a random sample of posts from each sentiment category and review them to ensure accuracy. This step helps confirm that the sentiment tool is classifying posts correctly.
Combine with Qualitative Analysis: If possible, use a tool that combines qualitative insights with sentiment scoring. For instance, a tool that allows you to view keyword associations or common phrases within each sentiment category can help you spot recurring themes.

Step 8: Report Findings with Clear Visuals and Narratives

Once your analysis is complete, it’s time to communicate your findings. Use clear visuals like pie charts, sentiment timelines, and word clouds to showcase trends. Your audience will appreciate the mix of visuals and narratives, especially if they’re new to sentiment analysis.

Tips for Effective Reporting:

Use Storytelling Techniques: Explain shifts in sentiment over time as a narrative, detailing how external events might have influenced public opinion.
Highlight Key Insights: Summarise the most critical findings upfront, such as unusual spikes or trends that align with specific events.
Provide Real-World Implications: Demonstrate how your findings can impact real-world outcomes, such as brand strategy, policy development, or public engagement.

Step 9: Reflect on Limitations and Future Research Directions

Social media sentiment analysis, while powerful, has its limitations. Language nuances, cultural context, and the inherent bias of algorithms are all challenges to consider. Concluding your research with a reflection on these limitations and possible future research avenues strengthens the academic rigor of your work.

Tips for Reflection:

Acknowledge Ambiguities: Note instances where the tool struggled, such as with sarcasm or mixed sentiments.
Suggest Follow-Up Studies: If your findings reveal emerging questions, mention them as potential follow-up studies. For example, if sentiment varied drastically across regions, further analysis might look at location-based differences.

By adhering to a structured approach and leveraging the right analytical tools, you can extract invaluable narratives that reflect the sentiments of diverse audiences.

Want to explore Leximancer for your sentiment analysis? Book at demonstration today.

Julia Ligteringen