Sentiment analysis is a methodology that extracts feelings, moods, opinions, and other types of subjective information from data. Also known as “opinion mining”, it is a sub-area of natural language processing (NLP) that combines text analysis, computational linguistics, and biometrics to detect, extract, measure, and analyze emotional states and personal opinions.
Data used in sentiment analysis typically comes from public online sources and applications, including article and blog comments, product reviews, public social media posts, mobile apps, and forums as long as no personal data is being collected. When combined with web scraping, sentiment analysis can help companies extract unique business insights and optimize their pricing strategy, product offering, and customer service in nearly real-time.
How sentiment analysis works
Sentiment analysis aims to identify emotional states from texts, such as happy, sad, angry, and surprised. These subjective feelings are then used to classify the text as positive, negative, or neutral. Besides identifying and classifying specific emotions, the procedure also assigns a sentiment level by scoring those states.
For example, an ecommerce company can extract thousands of comments about a particular jeans model or style from various public online sources. Sentiment analysis can be performed on that data to determine how users feel about those jeans by identifying keywords and assigning a score to each word. This allows data analysts to ascertain how customers experience the product’s color, fit, quality, and other features.
Data analysts and researchers perform sentiment analysis in two primary ways:
Method 1: Machine learning (ML)
The most prominent sentiment analysis technique uses supervised machine learning algorithms such as the Support Vector Machine or the Naive Bayes Classifier that are trained on labeled datasets.
Datasets are first created by labeling each piece of text as positive, negative, or neutral based on the sentiment expressed. The machine learning algorithm is then trained on the dataset to learn patterns and features that indicate the text’s sentiment. Once the process is completed, the trained algorithm can analyze new texts and make predictions based on previously learned patterns and characteristics.
Example:
Let’s say we want to extract sentiment from anonymous user-generated reviews of the jeans mentioned above. First, we will have to scrape a large dataset of jeans reviews that have already been labeled as positive or negative. This can be accomplished by visiting a large marketplace, filtering one and five-star reviews from the comments, and extracting that data using web scraping.
Next, a machine learning algorithm should be used to identify text patterns associated with positive or negative opinions. Once trained on one portion of the dataset, the algorithm can then be tested on another part to measure its effectiveness.
Throughout the process, the algorithm continues learning while researchers feed it additional data to increase its accuracy.
Method 2: Lexicon-based approach
Sentiment analysis can also be performed using a sentiment dictionary or “lexicon” containing a word list with associated sentiment scores.
The sentiment score assigns a degree of positivity or negativity associated with each word. The overall sentiment value is then calculated by aggregating individual scores of all the words in the text.
Example:
Let’s now use a lexicon-based method to evaluate how our users feel about their jeans.
For this example, let’s use the following comments:
- “These jeans are pretty good.”
- “These jeans are amazing!”
The comments are first processed to remove irrelevant words like “these” and “are”. Next, the sentiment lexicon assigns scores to the remaining terms.
In this example, the lexicon may assign a score of +5 for “amazing” and +2 for “good”. The degree of sentiment is then determined by aggregating all the scores.
Depending on the product being analyzed, this method may have some drawbacks. For example, researchers may misunderstand some terms used by specific cultures or age groups. This may result in excluding or miscategorizing critical words from the lexicon.
For example, teenage slang contains words like “slay” or “sick” that denote positive sentiment, i.e., “these jeans slay”. The algorithm may have some trouble here because the word “slay”, in its strictest definition, means to severely harm a human being or animal. However, in the context of its slang-based definition, it means that the user was exceptionally satisfied with the product.
Constantly changing language is one of the challenges that complicate the process. Despite any drawbacks, using a lexicon-based technique to extract sentiment is still effective and is usually faster and easier than machine learning.
Applications of sentiment analysis
Sentiment analysis benefits anyone wanting to glean subjective insights from extracted data. As increasingly more data is generated by and gathered from digital sources, the use of sentiment analysis is likely to increase in value.
Some specific use cases include:
Stock market predictions
Alternative data gathered from publicly available sources is successfully used by the leading financial services companies to indicate sentiments that drive the stock market and to alert investors about emotions and biases that can influence their decisions.
For example, external factors such as publicly available information from news sources, social media, and forums can have a widespread effect on stock price movement — especially when there’s a crowd psychology effect. CNN has even developed a Fear & Greed Index, which is based on the idea that investors tend to be emotional and reactionary. The index is used to measure the mood in the market and calculate fear.
Furthermore, a scientific study found that neural networks-based sentiment analysis performed substantially better for stock prediction than traditional models such as a boosted regression tree. Researchers in the study collected stock-related public tweets and obtained an average sentiment value using a supervised learning algorithm trained with closing stock index prices from Apple Inc. and the Dow Jones Industrial Average. Study results revealed that public tweets played an important role in predicting stock market movement and that neural networks perform better than the boosted regression tree.
Product and service marketing
Customer satisfaction is central to success for product and service-based businesses, making sentiment analysis an increasingly popular tool in marketing strategies.
Applications in this sector include:
- Product and service improvement
- Optimizing customer service
- Monitoring brand reputation
- Gathering sentiment data for marketing campaigns
- Improving packaging or measuring effectiveness of existing methods
- Measuring the success of discounts, bundles, and related strategies
- Setting prices and forecasting demand
- Competition analysis
- Predicting economic trends and calculating fears
- Determining the effects of changing prices (demand elasticity)
The production of data continues to grow as the customer experience becomes further digitized. As a result, sentiment analysis can be used to gain insights into all aspects of the customer journey and product lifecycle.
Summary
Sentiment analysis can produce critical insights that benefit decision-making in both business and public organizations, especially if performed using fresh data. Recent developments in AI and ML have drastically improved the speed and accuracy of the opinion mining process, allowing organizations to scan massive datasets in a time span that was previously unimaginable.
However, sentiment analysis’ success hinges on the organization’s ability to extract large-scale, good-quality data from public sources. Therefore, it would be impossible without web scraping technology, which provides data analysts with real-time intelligence. Powered by ML and state-of-the-art scraping software, sentiment analysis is becoming one of the key ways to understand market trends, consumer behavior, and even political attitudes.
