What is sentiment analysis in NLP?

What is sentiment analysis?

Sentiment refers to the underlying attitude or emotion conveyed by a writer in a text, and therefore, sentiment analysis means analyzing or deducing the writer’s sentiment based on the text. Being one of the key techniques in natural language processing, sentiment analysis is crucial in today’s data-driven world, where it helps companies, researchers, and organizations understand public opinion, customer satisfaction, and social trends. By analyzing sentiments in real-time, businesses can make informed decisions, tailor their services to meet customer needs, and respond quickly to public feedback, impacting everything from marketing strategies to product development.

Why do we use sentiment analysis?

Sentiment analysis is needed majorly for the following reasons:

1. It helps make sense of vast amounts of unstructured data, capturing key insights without requiring structuring of the data structure. For example, businesses can analyze thousands of product reviews to understand customer satisfaction and identify common product issues.

2. It identifies and addresses real-world issues in real time. For instance, a travel company could monitor customer feedback during peak holiday seasons to address complaints immediately, enhancing customer satisfaction and loyalty.

Types of sentiment analysis

Sentiment analysis can be broken down into several types, each focusing on different aspects of emotional and opinion-based text analysis. The following are the types of sentiment analysis:

• Fine-grained sentiment analysis: This type classifies sentiments on a more detailed scale, often from very negative to very positive. For example, on a scale from 1 to 5, a rating of 1 could represent “very negative", 3 as “neutral”, and 5 as “very positive”. This granularity allows businesses to understand intensity levels of feedback, such as distinguishing between mildly dissatisfied and highly dissatisfied customers.

• Emotion detection: Rather than simply categorizing sentiment as positive or negative, emotion detection identifies specific feelings expressed in the text, such as happiness, sadness, anger, or surprise. It is particularly useful for applications where understanding the emotional undertone is essential, such as customer service, social media analysis, or therapeutic settings.

• Aspect-based sentiment analysis: This type focuses on identifying sentiments about specific components or “aspects” within a text. For instance, in a product review that mentions “battery life” and “design,” aspect-based analysis could determine that the user feels positive about the design but negatively about the battery life. This approach is valuable for product feedback, as it helps businesses pinpoint specific areas of praise or concern.

• Multilingual sentiment analysis: Multilingual sentiment analysis processes texts written in different languages to identify positive, negative, or neutral sentiments. This is crucial for businesses operating globally, as it allows them to analyze customer opinions and sentiments from multiple regions.

Sentiment classification

We can deduce multiple sentiments from a text passage. However, a more straightforward classification would be to separate the text into either positive, negative, or neutral categories.

The following table demonstrates how text can be classified into the categories mentioned above:

How to build a sentiment classifier from scratch

We can implement sentiment analysis using TensorFlow. We can follow the following steps to build a classification model:

Step 1: Import the requisite libraries

Import the TensorFlow library using the following code:

Step 2: Define the class

Make a class that classifies the text passages. After that, the input text is tokenizedTokenization is the process of breaking raw text into smaller chunks. using the Tokenizer function from the Keras library:

• Line 5: The num_words is the number of vocabulary words.

Step 3: Convert text to sequence

Now, convert the input text into a sequence using the following code:

• Line 2: The fit_on_text() function updates the internal vocabulary based on the list of texts.

• Line 3: The input list texts is converted into an integer sequence using the text_to_sequences() function.

Step 4: Generate training pairs

Similar to standard classification, text classification involves creating training pairs of input data and labels. In this context, the input data consists of tokenized text sequences, with each sequence labeled according to its category. For simplicity, the category labels can be represented as integers in the range $[0,n−1]$ where $n$ is the total number of classes.

The following example shows three training pairs in which the maximum length of the sequence is four, and the texts are classified into two categories:

Now that we understand what are training pairs, let’s create the training pairs for the classification model using the following code snippet:

Sentiment classification with pre-trained models

While building a sentiment analysis model from scratch allows for a lot of customization, it is not feasible for complex and time-sensitive problems. Python packages offer many pre-trained sentiment analysis models which can be integrated into the model using a few lines of code. For instance, the following code uses the pipeline class to make predictions:

It outputs the following results:

We can also specify other models which are better suited to our use case and language.

Applications of sentiment analysis

Sentiment analysis has a wide range of applications, including:

1. Social media monitoring: Analyzing comments and reviews on platforms like X (formerly Twitter) and Facebook to categorize sentiments as positive, negative, or neutral, providing insights into public perception.

2. Customer reviews: In the app stores, sentiment analysis enhances customer service by analyzing user reviews to identify common issues and areas for improvement.

3. Market research: Businesses use sentiment analysis in product reviewing to gauge public opinion and adjust marketing strategies based on consumer feedback.

4. Professional reviews: Reviewers leverage sentiment analysis to summarize overall product sentiment based on aggregated user feedback.

Conclusion

As the digital landscape continues to evolve, sentiment analysis presents exciting opportunities to understand human emotions across various contexts. Beyond the basics covered here, there are fascinating areas to explore, such as sarcasm detection, measuring emotion strength, and analyzing sentiments in audio or video. Combining sentiment analysis with technologies like real-time translation and multimodal emotion detection could lead to richer insights and open new avenues for innovation.