What is Sentiment Analysis in NLP? How it works & Benefits

In today’s digital age, understanding customer emotions and opinions is extremely important for the sustainable development of businesses. Sentiment Analysis has emerged as a useful tool to help businesses capture customer feedback quickly and effectively. This article by FPT.AI will help you better understand Sentiment Analysis, how it works, and the outstanding benefits it brings.

What is Sentiment Analysis?

Sentiment Analysis is the process of using Natural Language Processing (NLP), Machine Learning (ML), and data analysis techniques to identify and classify emotions (positive, negative, neutral) hidden in text. The goal of Sentiment Analysis is to understand the emotions and attitudes of users or the community towards a certain product, service, or topic.

Current applications of Sentiment Analysis include:

• Monitoring customer sentiment on social media to gauge satisfaction with a brand or product.
• Analyzing customer feedback via email, chat, and online reviews to improve service.
• Measuring the effectiveness of marketing campaigns, based on user sentiment towards advertisements and media content.
• Detecting brand crises early by analyzing negative sentiment spikes across media channels.
• Evaluating new products or services, helping businesses adjust their development strategies based on user feedback.
• Analyzing competitors, to understand how customers feel about competitors’ products or services.
• Monitoring market trends and spotting business opportunities based on positive or negative feedback about similar products.
• Improving customer experience by detecting specific issues and responding promptly.

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How Sentiment Analysis Works

Sentiment Analysis works by using machine learning algorithms such as Naive Bayes classifiers, decision trees, or neural network models. To understand how it works in more detail, here are the main steps of the mechanism:

Data Collection

Textual data is collected from sources such as social networks, forums, product reviews, emails, or other documents. This step is important to have enough information for sentiment analysis.

Data Preprocessing

Data Preprocessing is an important step in preparing raw data before feeding it into machine learning models or analysis. The goal of this process is to clean, transform, and organize the data to ensure high quality and easy to process. Here are the main steps in data preprocessing:

• Tokenization: The text is broken down into words, phrases, or sentences. Each of these units is called a “token,” making it easier for the system to analyze.
• Stop Words Removal: Common words like “and,” “but,” and “is” that do not carry much emotional value are often removed.
• Lemmatization: Words are converted to their original form, for example “run” and “run fast” are both converted to the original word “run.” This helps the system understand the exact meaning of the word without being affected by its variant form.
• Normalization: Converting uppercase letters to lowercase, handling punctuation, and special characters to avoid semantic misunderstandings.

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Feature Extraction

Once the data has been processed, the next step is to extract features from the text. This may include:

• Word Vectorization: Using methods such as Bag of Words (BOW) or TF-IDF to turn words into numerical forms that can be processed by algorithms.
• Word Embedding: Using models such as Word2Vec or GloVe to create embedded word vectors, representing words in a numerical space with their context.

Classification Model Application

Classification Model Application is the process of using machine learning models to label or classify data samples into different groups or classes. The goal of a classification model is to predict the label of unknown data based on patterns learned from training data. There are 2 types of models:

• Machine Learning models: Algorithms such as Naive Bayes, SVM (Support Vector Machine), or Deep Learning models such as LSTM and BERT are used to classify text by sentiment (positive, negative, or neutral).
• Deep Learning models: More advanced models such as BERT, Transformer are capable of understanding complex semantics and context of text for more accurate classification.

Model Evaluation & Tuning

After being fully built, the model will be evaluated through indicators such as Accuracy, Recall, Precision, and F1 Score. If the model does not meet the requirements, it will be calibrated through parameter adjustment or testing with other algorithms.

Deployment & Application

Once the model meets the requirements, it will be deployed and applied in practical applications such as customer feedback analysis, social media sentiment tracking, or automatic classification of product reviews.

Main types of Sentiment Analysis

Fine-Grained Sentiment Analysis

This is a form of sentiment analysis that allows for the classification of sentiment levels on a range of levels rather than just “positive” or “negative.” For example, a classification system might determine that a comment is “very positive,” “positive,” “neutral,” “negative,” or “very negative.” Here is a typical example of fine-grained sentiment analysis:

In a marketing campaign, users rate a product on a 5-star scale. Fine-Grained Sentiment Analysis would interpret this rating as follows:

• 5 stars: “The product is excellent, the quality exceeded expectations” (Very Positive).
• 3 stars: “The product is okay but not exceptional” (Neutral).
• 1 star: “Totally disappointing, not worth the money” (Very Negative).

Aspect-Based Sentiment Analysis

This is a type of analysis in which the system not only determines the overall sentiment but also analyzes the sentiment about specific aspects or elements of a product or service. These aspects can be quality, price, service, etc. A concrete illustration of Aspect-Based Sentiment Analysis is shown by how a user reviews a restaurant. If a user comments “The food is great but the service is too slow”, Aspect-Based Sentiment Analysis will determine that the customer’s attitude towards the Food is positive but towards the Service is negative.

Emotion Detection

Emotion detection is the identification of specific emotions contained in text, such as happiness, sadness, anger, anxiety, or fear, rather than just determining the degree of positivity or negativity. A typical example of emotion detection is when a user posts on social media: “I am really excited to receive this news!”. The emotion detection system will identify the person’s attitude as excited (happy, excited).

Intent Analysis

Intent analysis is the detection of the user’s purpose or intention through text. This is often used in chatbots, virtual assistants to determine the action the user wants to perform (purchase, ask for information, request support, etc.). An example of this type of Sentiment Analysis is when a user sends a message: “I want to book a table for dinner at 7 pm tonight.” Intent analysis will identify the intention to book a table and forward this request to the reservation management system.

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What are the key benefits of Sentiment Analysis?

Sentiment Analysis not only helps businesses collect and analyze data from sources such as social media posts, product reviews, emails, and many other channels, but also provides insights into customer sentiment and opinions. By analyzing emotional responses, businesses can leverage this information to make strategic decisions, improve services, and optimize business operations.

Here are the outstanding benefits that Sentiment Analysis brings to businesses:

• Better understand customer sentiment towards products, services, or brands.
• Early detection of potential problems in customer experience for quick corrective action.
• Improve marketing strategies based on user sentiment feedback, helping to optimize campaigns.
• Enhance customer care by promptly handling negative feedback.
• Measure the effectiveness of a new product or service by analyzing post-launch sentiment and feedback.
• Reduce the risk of a media crisis by monitoring negative sentiment in online conversations.
• Make more accurate data-driven decisions by relying on sentiment analysis from real users.
• Analyze competitors to understand how customers feel about their products.
• Optimize customer experience through direct feedback, helping to adjust products/services.
• Predict market trends, helping businesses capture new business opportunities from changing consumer sentiment.

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Leveraging Sentiment Analysis to Breakthrough Operations with FPT AI Engage

FPT AI Engage is a smart virtual assistant solution applying Artificial Intelligence (AI) developed by FPT Smart Cloud, widely deployed at customer care centers of many large enterprises in Vietnam. Applying Sentiment Analysis technology, FPT AI Engage has been honored at the prestigious international award Asian Technology Excellence Awards in the category of Artificial Intelligence Technology. This has affirmed the position of the solution as one of the leading AI virtual assistants.

With comprehensive automation capabilities, FPT AI Engage can smoothly handle both outbound calls and inbound calls, and integrates a smart Interactive Voice Response (Smart IVR) with an accuracy of up to 92%. Powered by advanced artificial intelligence technologies such as natural language processing (NLP), speech synthesis, speech recognition, conversation management, and sentiment analysis, FPT AI Engage is capable of operating effectively in multiple languages, including Vietnamese, English, Japanese, and Bahasa Indonesia.

Not only performing simple tasks such as answering frequently asked questions or conducting customer surveys, FPT AI Engage can also handle more complex tasks that require high professionalism such as product/service consulting, payment reminders or debt collection. Thanks to its flexibility and deep integration into customer care call centers, FPT AI Engage has created important breakthroughs in business operations, bringing optimal experiences to end users.

Currently, this solution has been deployed for more than 100 businesses, automating more than 120 million conversations per month. FPT AI Engage helps businesses save up to 40% of operating costs and increase call center productivity by 60%. This is the key for businesses to move forward in the digital age with outstanding efficiency.

To better understand FPT AI Engage and AI Chatbot, please contact FPT.AI via hotline or directly here:

• Address: FPT Building, No. 10 Pham Van Bach, Dich Vong Ward, Cau Giay District.
• Email: support@fpt.ai
• Hotline:1900 638 399

In short, Sentiment Analysis is a powerful tool that helps businesses not only understand customer emotions but also optimize many aspects of operations and business strategies. From capturing user psychology, detecting potential problems early to personalizing services and increasing sales efficiency, Sentiment Analysis has become an indispensable part of the digital age. FPT AI Engage is the optimal solution to apply Sentiment Analysis technology for businesses. Contact FPT.AI now to get the best solution!

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