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Twitter Sentiment Analysis: 5 Best Use Cases and Tools
Sentiment analysis for Twitter (now called X) is a method for analyzing customers’ perceptions of your business. The method primarily involves identifying sentiment polarities such as positive, negative, or neutral in conversations and classifying them based on the sentiment.
Analyzing sentiment on X can be your strategic advantage in acting upon feedback from a pool of more than 611 million monthly active users. Not to mention, Twitter sentiment analysis can be a great addition to your voice of the customer methodology.
In this post, you will see what Twitter sentiment analysis is, how it works, and how to implement it for your brand in just three steps.
- What is Twitter sentiment analysis?
- 5 Ways Twitter sentiment analysis can improve your business strategy
- How to implement Twitter sentiment analysis for your brand in 3 steps
- How Twitter sentiment analysis aligns with C-suite decision-making and corporate strategy
- 3 Twitter sentiment analysis tools that offer advanced insights
- Why Twitter sentiment analysis is a must-have for brands
What is Twitter sentiment analysis?
Twitter sentiment analysis is the process of detecting and classifying the emotional tones — positive, negative, or neutral — expressed in tweets, now called posts on X. It is also called opinion mining.
For example, check out the two reply threads on different X posts: the left is a thread of a tech journalist sharing about a free course on AI/ML, and the right is of a credit card ad!
Can you find any differences in the underlying sentiments in the two streams of conversation?
If you read closely, you will see an overall positive sentiment on the free course thread (left). But the thread on credit cards (right) leans towards negative sentiments.
This is precisely Twitter sentiment analysis. For the longest time, this was a manual process riddled with technical setups (and datasets) or reliant on human teams analyzing individual X posts for sentiment.
But as you just noticed, analyzing the sentiments of each reply, thread, or post, which requires close reading, is a tedious process and almost impossible on an enterprise scale without a sizable team!
Thankfully, with recent AI advancements, you can analyze X sentiment 24/7 using natural language processing (NLP) and machine learning without any human inputs and scope of human errors.
How does Twitter sentiment analysis work?
Twitter sentiment analysis involves five broad steps (regardless of the analysis methods, which are discussed below):
- Data collection: Gathering relevant posts using X’s API, third-party tools, keywords, hashtags, and handles
- Preprocessing: Cleaning the data, such as removing irrelevant characters, converting text to lowercase, and handling emojis, slang, or misspellings
- Extraction and model training: NLP extracts required features from the tweets for sentiment analysis, such as keywords and other contexts. You train NLP to identify patterns and links between X text and sentiment
- Sentiment classification: The trained model can now analyze the words, tone, and context of any post using Python libraries, machine learning models, or rule-based approaches
- Drawing insights: You can visualize trends in sentiment and apply insights to optimize marketing strategy, control brand perception, and improve customer experience
You can do Twitter sentiment analysis broadly in three ways:
1. AI-enabled social listening platforms with firehose access to X that automatically does the steps mentioned above (🐇 Fastest)
2. Custom-coded sentiment analysis setups using Python and NLP libraries (⏲️ Time-consuming)
3. No-code sentiment analysis setups with third-party integrations (⏲️ Time-consuming)
Custom-coded setups for Twitter sentiment analysis require a slew of third-party and native tools and lengthy processes:
1. “Cleaning” the inputs (X conversations)
2. Setting up dependencies (Python libraries to gather tweets, visualize them, and generate word cloud)
3. Procuring X API to authenticate with the platform
4. Third-party API to analyze sentiment for conversations
Using no-code setups is easier than coded setups but still requires you to integrate X and a sentiment analysis API such as Mimecast or Google Cloud, via a third-party platform such as Zapier for sentiment analysis.
🔥 If you’re using Sprinklr - You have Firehose access to X API for plug-and-play sentiment analysis! On Sprinklr Insights, simply create a new listening topic with X as its source to start analyzing Twitter sentiment.
5 Ways Twitter sentiment analysis can improve your business strategy
Here are five ways to use sentiment analysis for Twitter:
1. Crisis management and PR
⚡For: Mitigating negative sentiment before it escalates
You need to detect and respond to negative sentiment around your brand in real time to stay above PR crises. Monitoring sentiment in X conversations is one way to take proactive action before issues spiral out of control.
In 2021, Chipotle faced backlash after reports of foodborne illness outbreaks linked to its restaurants surfaced.
The food chain tracked the spike in negative sentiment around the issue on X and swiftly addressed concerns, emphasizing food safety measures, and providing reassurance through customer service teams and Twitter statements.
Chipotle’s timely response saved the situation from escalating and protected customer trust.
2. Competitive intelligence
⚡For: Unearthing action-ready insights from customers’ perceptions of your competitors
Sentiment analysis is one of the best ways to understand how your customers feel about your competitors and identify weaknesses and opportunities.
If a certain sentiment around your competitor’s product or service leans toward negative, it could be an opportunity to refine your brand positioning, fill gaps in your communications, and position your brand as a better choice to capture market share.
For example, Tesla tracked public sentiment around its competitors, such as Rivian, when the latter launched its electric truck.
By analyzing social media conversations, Tesla learned about potential customer frustrations with Rivian’s delivery delays and used it as an opportunity to highlight their faster production timelines.
💡Pro tip - Use a competitive benchmarking tool tocompare how customers feel about your brand versus your competitors. Track sentiment around your brand and theirs to uncover market gaps, and brand reach, identify opportunities, and optimize your strategy.
3. Customer experience
⚡For: Enhancing customer support and increasing engagement
Your customers often turn to X to voice complaints, ask questions, or share feedback. Sentiment analysis can help you identify and address underlying emotions in real time, improving customer satisfaction.
Negative sentiment can indicate customer pain points, which you can use as a tip to optimize support and offer quick responses.
Have you ever gotten a response from your airline after voicing concerns about travel disruptions caused by a technical snag on X?
That’s your airline probably using sentiment analysis to track your complaints on the platform and prioritize the most frustrated voices for direct responses and offer solutions. This proactive measure improves the customer experience and reduces negative sentiment around a brand.
4. Marketing strategy
⚡For: Personalizing campaigns based on sentiment groups and trends
You can analyze sentiment trends to create more engaging, emotionally resonant marketing campaigns targeting groups. Sentiment analysis also helps fine-tune campaign messaging, say, for neutral customers, and drive engagement.
For example, stc, a Riyadh-based telecommunication company, used AI-driven sentiment analysis to accurately analyze Arabic sentiments from a mix of dialects in social media.
Using AI and automation, stc can categorize sentiments and automatically tag them with the products and services they belong to for granular insights.
📗 Learn more: Harnessing AI-driven sentiment analysis for smarter customer engagement
5. Product development
⚡For: Refining offerings based on consumer sentiments
Tracking sentiment in product or service-related conversations can help you understand which features or services resonate with customers, guiding product development decisions.
You can use this sentiment data to validate product ideas, improve existing offerings, and ensure that new launches meet customer expectations.
Oreo engages its X followers by conducting polls to gather customer feedback on potential new flavors. This direct interaction allows Oreo to assess consumer interest, preferences, predict top-selling limited-edition products, and invest in similar campaigns in the future.
💡 Pro tip: Use a Social listening tool to tap into real-time conversations around your new offering, an idea, or a pre-launch campaign and understand how customers feel about it. Use the data to detect customer sentiment, compare brand performance, and identify themes to inform your product roadmap.
📚 Also read: 4 Sentiment analysis examples to improve your customer experience
How to implement Twitter sentiment analysis for your brand in 3 steps
For this demonstration, we are showing you how to implement Twitter or X sentiment analysis for your brand using Sprinklr Insights.
The process involves adding X as a listening source, which unlocks sentiment data, among others.
Step 1: Create a Topic with X (or Twitter) as its source
Click the New Tab icon. Under Sprinklr Insights, click Topics under Listen.
Step 2: Trigger a new topic creation process
Click + Add Topic, and when the new screen opens, enter basic details.
At the bottom of the Topic Details tab, click the Setup Query tab. On this tab, fill in the required fields to build your query.
Click Next or directly click the Apply Filters tab.
Step 3: Apply Twitter filters
Select Twitter as the listening source. Here, you can filter your results by Sources, Languages, Locations, and other filters for the platform. Fill in the required details.
In the bottom left corner, click the toggle icon to set the fetching status of your topic as Fetching or Not Fetching.
Click Save Topic. Congratulations, you can now analyze Twitter sentiment and many other platform-specific data.
📖 Also read: Understanding the nuances of sentiment: How generative AI can help brands navigate public perception
How Twitter sentiment analysis aligns with C-suite decision-making and corporate strategy
Here are some ways Twitter sentiment analysis can impact your brand's strategic decision-making:
1. Driving customer-centric decisions: Insights from customer sentiments can directly influence your strategic direction of the company for product, marketing, etc.
2. Shaping strategic pivots: Sentiment analysis can provide timely feedback, such as during product launches, that can inform you (or the C-suite) to make agile adjustments to strategic plans in response to customer reception.
3. Informing product and service refinement: For example, identifying pain points from sentiment analysis has been instrumental in refining stc's — a Riyadh-based telecom brand — products and services.
4. Enabling proactive responses: Sentiments can lead you to identify trends, detect shifts in customer mood, respond proactively to brewing issues or opportunities, and make strategic decisions confidently.
5. Enabling personalization for loyalty: Individual preferences and behaviors uncovered from the sentiment analysis can help you design personalized user journeys for deeper engagement and loyalty.
6. Innovate customer-centric strategies: AI-driven sentiment analysis could be your catalyst for innovative customer-centric strategies.
Also read 📚: 4 Sentiment analysis examples to improve your customer experience
3 Twitter sentiment analysis tools that offer advanced insights
Here are three Twitter sentiment analysis tools that offer advanced insights:
1. Sprinklr Insights
💸 Pricing - Custom
🔑 Key features
- Industry-best data coverage
- Features built for enterprise-grade X analysis
- Verticalized gen-AI capabilities for insights with 90%+ accuracy
- Integrates with the unified-CXM platform for an org-wide flow of insights
Sprinklr Insights is a comprehensive consumer intelligence suite with AI-driven sentiment analysis capabilities from social media like X.
You can get accurate, real-time insights of consumer sentiments from X and across multiple platforms for granular segmentation and advanced reporting.
2. Medallia
💸 Pricing - Custom
🔑 Key features
- Sentiment analysis across touchpoints
- Capabilities focus on experience
- Offers dedicated employee listening
Medallia focuses on experience management and uses sentiment analysis to understand customer feelings across various touchpoints. Its sentiment analysis integrates with CRM systems for a holistic view of your customer’s journey.
3. Qualtrics
💸 Pricing - Custom
🔑 Key features
- Analysis for very strong sentiment polarities (e.g.: very positive)
- Sentiment analysis is built-in for cross-platform workflows
- Comprehensive experience management suit
Qualtrics offers a comprehensive experience management platform, incorporating sentiment analysis into its survey and feedback tools.
You understand customer sentiments and identify areas for improvement from beyond social media content.
🔎 Find more: 10+ best sentiment analysis tools for 2024
Why Twitter sentiment analysis is a must-have for brands
Twitter sentiment analysis is a critical tool for brands that want to remain competitive:
- Long-term brand positioning: Sentiment analysis assists your brand in keeping up with customer sentiment, maintaining consistency, and building a market presence informed by the customers' voices.
- Competitive advantage: Competitor sentiment tracking lets your brand spot gaps in products or services and modify its offerings to match or exceed its competitors' offerings.
- Predictive capabilities: AI, ML, and NLP built into sentiment analysis tools can help you predict consumer behavior changes, address potential risks, and maximize campaign effectiveness.
Are you doing Twitter sentiment analysis yet? Get started with Sprinklr Insights
X has long been a social media platform for business importance, thanks to the large user-base and the community that loves to voice their opinions.
If your business is active on X or your customers are, sentiment analysis on the platform could be a strategic advantage, especially if your competitors aren’t that active on the platform.
What was a manual, coded, and strenuous process a few years ago is now almost plug-and-play with AI and machine learning. Twitter sentiment tracking is not just easier with Sprinklr Insights but more accurate and actionable.
Try the Sprinklr Insights demo and transform your social listening efforts.
