Sentiment Analysis: A Step-by-Step Guide (Updated 2024)

Sentiment analysis, also known as “opinion mining,” is the automated process of analyzing text to interpret the sentiments behind it. Through machine learning and text analytics, algorithms can classify statements as positive, negative, or neutral and they do so quite effectively: artificial neural networks achieve 85% accuracy in identifying sentiment, the highest among other methods. Using this highly effective technology, companies and brands are able to monitor consumer sentiment, benchmark against competitors, and make more informed decisions.

In this blog, we’ll cover each step of sentiment analysis, discuss common challenges, and highlight key features to look for in a sentiment analysis tool.

Steps of Sentiment Analysis

Sentiment analysis involves several key steps to analyze and interpret data related to a company or brand. Let’s explore this process.

Step 1: Data Gathering

The first step is to collect the data that will be analyzed. This data can be sourced from social media platforms like Twitter using scraping tools, APIs, or customer data feeds. Additionally, user reviews from services such as Google and Yelp can provide valuable insights. The goal is to gather all mentions of the company or brand over a specified period.

Step 2: Text Cleaning

The next step is to clean the collected data to prepare it for analysis. Text cleaning tools help by removing stop-words (e.g., a, and, or, but), punctuation, and performing stemming. This process strips the text of irrelevant elements, ensuring that only meaningful content is analyzed.

Step 3: Sentiment Analysis (Opinion Mining)

With the data cleaned, the next step is to apply sentiment analysis algorithms. These algorithms classify the data on a spectrum from “positive” to “negative” and can also detect more nuanced emotions like anger or sadness. The sentiment library used by the algorithms helps identify opinions and categorize them accordingly.

Step 4: Understanding the Results

Finally, the results of the sentiment analysis are grouped into major categories such as positive, neutral, or negative reactions. Each sentiment is tagged with its original date, allowing you to visualize the data over time. This timeline can reveal “peaks” (surges of positive sentiments) and “valleys” (surges of negative sentiments), helping you correlate specific events with changes in public opinion.

For instance, an airline might notice a spike in Twitter mentions due to viral content. By gathering these mentions and performing sentiment analysis, the company can gauge public reaction. Understanding whether the reaction is positive or negative informs the company’s next steps:

• Positive Reaction: The airline might capitalize on the moment with a new commercial campaign or by pitching the content to the news media.
• Negative Reaction: The airline may need to address the issue promptly to prevent a brand crisis, possibly by taking corrective action or issuing a public statement.

This process underscores the importance of sentiment analysis in making informed, timely decisions based on public sentiment.

Case Study: Converse All Star

Let’s take a look at a sentiment analysis of the Converse Chuck Taylor All Star Core Ox athletic shoes to understand what drives their 5-star reviews and how they can improve.

Top Drivers for 5-Star Reviews:

• Fit
• Comfort
• Shipping

Conversely, the product could achieve significantly more 5-star reviews by addressing the following issues:

• True to Size: Ensuring the shoes are true to size could reduce 1-2 star reviews by 28%.
• Authenticity: Addressing issues with counterfeit sales could decrease 1-2 star reviews by 12%.
• Durability: Improving the durability of the shoes could lower 1-2 star reviews by 9%.

By addressing these areas, Converse can measurably and quantifiably boost its product ratings. Here’s how:

• Sentiment Analysis: Use sentiment analysis to identify and understand customer feedback at the product level.
• Review Comparison: Cross-check low product ratings with high product ratings to pinpoint specific areas for improvement.

This approach offers a data-driven strategy to enhance product ratings and customer satisfaction in industries that rely on consumer reviews.

Challenges of Sentiment Analysis

Interpreting text accurately is a significant challenge for sentiment analysis tools. Here are some specific issues that illustrate why this can be difficult.

Sarcasm Detection

Consider the statement: “We had to wait 45 minutes to get a table. Great!” A human reader understands that the word “Great!” is sarcastic because of the context provided by the long wait time. However, without the ability to recognize sarcasm, a sentiment analysis tool might incorrectly classify this comment as positive. Effective sentiment analysis tools need to account for context to accurately detect sarcasm and avoid generating misleading data.

Nuance and Negators

Nuanced language can also pose challenges. Take the comment: “The movie was not bad.” While it literally suggests the movie wasn’t bad, it implies that the movie exceeded low expectations, which is a more subtle form of praise. This use of a “negator” can be tricky for sentiment analysis tools to interpret correctly without understanding the underlying implications.

Intensifiers

Intensifiers add another layer of complexity. For instance, the statement “The company’s comment on this issue was pretty good” carries a different sentiment than “The company’s comment on this issue was good.” The word “pretty” softens the positive sentiment, introducing a nuance that might be missed by less sophisticated analysis tools.

Basic sentiment analysis tools often fail to capture the complexity of human emotions expressed in text. To achieve accurate sentiment analysis, it’s crucial to use advanced tools that can understand context, detect sarcasm, and interpret nuances in language. This ensures a more reliable and insightful analysis of customer sentiments towards your brand.

Tools and Technologies for Sentiment Analysis

When choosing a sentiment analysis tool, prioritize solutions that offer the following features:

• Holistic Approach: The tool should provide data collection, data cleansing, and analysis in one integrated platform.
• SKU-Level Analysis: It should offer sentiment analysis by individual products and features, not just by brand.
• Self-Service Capability: The tool should be user-friendly, allowing business users to operate it without requiring experts.
• eCommerce Focus: Prioritize tools that focus on eCommerce retailers as data sources, utilizing verified buyer feedback for accurate insights.

Here are some tools that are widely used for sentiment analysis, each offering unique features and capabilities:

• TextBlob: A simple library for processing textual data, providing a straightforward API for diving into common natural language processing (NLP) tasks.
• VADER (Valence Aware Dictionary and Sentiment Reasoner): A lexicon and rule-based sentiment analysis tool specifically designed for social media text.
• Google Cloud Natural Language: Offers powerful sentiment analysis capabilities as part of a broader suite of NLP tools, suitable for large-scale applications.
• IBM Watson: Provides a robust set of AI tools, including advanced sentiment analysis functionalities, which can handle various languages and domains.
• Revuze: For enterprises seeking an all-in-one option, Revuze automatically collects unstructured data from multiple sources, analyzes it, and organizes it into actionable insights.

Harness the Power of Sentiment Analysis for Business Success

In today’s digital landscape, social media mentions and product ratings drive online sales and higher conversion rates. However, finding a quantifiable, measurable way to impact these ratings is rare. Sentiment analysis offers a powerful solution, leveraging consumer feedback to provide actionable insights across various aspects of a business.

Sentiment analysis is not limited to a single retailer or a set of opinions; it provides industry-wide intelligence, covering your brand and competitors on multiple fronts—brand, product, purchase, and service. This comprehensive view allows for informed decision-making and strategic improvements.

To harness the power of sentiment analysis and transform your consumer insights into actionable strategies, schedule your demo with Revuze today.

Boaz Grinvald