The Power of Sentiment Analysis: How NLP Services Decode Customer Emotions

A method that has become established over time and is already used in many areas and industries is so-called sentiment analysis. In this article, we will explore the concept of semantic analysis and NLP in social media and look at specific examples of how this technology can be applied.

What is Sentiment Analysis?

Often, the emotional states are not recognizable at first glance but can hide between the lines, which is where sentiment analysis comes into play.

Sentiment analysis is part of the text mining discipline (a data mining subfield). Its main goal is to reveal the emotional orientation of a text by recording the moods and subjective attitudes expressed there and classifying them as positive, negative, or neutral using sentiment clusters. Sentiment analysis uses statistical methods, natural language processing (NLP) techniques, and machine learning (ML) to extract and evaluate text content’s opinions, feelings, and moods. 

Companies use sentiment analysis to obtain insights from:

• customer messages (emails, messengers, contact forms, etc.)
• call center interactions
• online customer reviews
• posts and comments on social media
• other web content

Check out two major unstructured data analysis types using data-based artificial intelligence (AI) and machine learning (ML) to make judgments and predictions.

1. Rule-based approach

The rule-based approach in sentiment classification uses an algorithm that identifies a detailed and clear opinion description. The approach includes identifying subjectivity, the polarity of opinions, and the opinion topic and includes some of the following possible operations:

• Stem form reduction (reducing words to their base form)
• Parsing (analyzing the grammatical structure of sentences)
• Tokenization (segmenting text into individual units or tokens)
• Part-of-speech tagging (assigning grammatical categories to words)
• Speech analysis (examining linguistic patterns)

The rule-based approach initiates with two sets of words: one exclusively comprising positive terms and the other solely featuring negatives. Utilizing predefined linguistic rules, the algorithm then yields results by identifying and categorizing sentiment expressions in text data, offering structured information regarding the sentiment polarity (positive, negative, or neutral) of the analyzed content. This approach proves beneficial for applications necessitating explicit sentiment labeling and enhanced interpretability.

2. Automatic approach

Unlike rule-based approaches, automatic sentiment analysis delves deeply into text, extracting nuanced data by striving to comprehend the meaning of a message. Thereby, it provides contextually rich insights that are valuable for applications requiring a more nuanced understanding of sentiment classification in diverse textual content. Instead of basing its work on predefined rules, automatic sentiment analysis strives to understand the meaning of a message. It uses machine learning algorithms to examine data and can involve the following classification types:

• Linear regression (employing linear models to predict sentiment)
• Support vector machines (using a decision boundary to classify sentiment)
• Recurrent neural network derivatives (e.g., long-term or short-term memory networks and gated recurrent units as specialized architectures for capturing sequential dependencies)

Things get pretty tricky when it comes to mood analysis, as it seems to be an extraction of abstract, changing, and highly subjective insights. Thus, getting an accurate idea of ​​the mood takes a lot of work. 

Apart from the specific approaches, sentiment analysis can be conducted manually or automatically. Let’s take a look at which option is worth choosing based on a few features.

Criteria	Manual analysis	Automatic analysis
Accuracy	High accuracy due to human evaluation, resulting in a low error rate. Human evaluators can comprehend nuanced language, context, and subtle emotional nuances	Accuracy relies on predefined algorithms and may vary based on data quality and model training
Effort and scalability	Resource-intensive, especially with a large volume of statements	Less human effort is required; scalability depends on software capabilities and processing power
Data Sources	Primarily suitable for smaller datasets; well-suited for in-depth analysis	Well-suited for large datasets from diverse sources like social media, rating platforms, and surveys

Emotional intelligence in monitoring conversations on social media has become essential for companies and organizations that need to stay informed about the opinions and interests of their customers and stakeholders. Organizations investing in cutting-edge data analytics infrastructure solutions can adopt sentiment analysis methods that will best resonate with their audience’s mood — and adjust their strategies as accurately as possible.

NLP Use Cases for Identifying Audience Sentiments

Sentiment analysis uses NLP technology. It identifies emotional statements in pieces of text written by users, such as product reviews and other posts, and assigns them to three categories: positive, negative, and neutral. Therefore, human attitudes towards a company, a campaign, or a product can be determined in real time. 

Implementing NLP in social media and other online assets for monitoring emotional reactions provides a valuable tool for brands to rethink or develop new strategies. Here are a few widely known examples.

1. NLP in public health and emergencies. In 2020, due to the rapid spread of COVID-19, we all began to see the value of large-scale public health data analysis. In those crises, social listening was important to recognize changes in social behavior quickly. Natural language processing in social media helps quickly assess public sentiment. For example, a recent project analyzed over 1,000 tweets using the keyword «masks» to understand how people think and feel about masks.
2. Sentiment analysis in marketing. In marketing, you must stay informed about your target audience’s thoughts and feelings. A 2019 study used Twitter sentiment analysis to evaluate customer engagement in clothing brands Nike and Adidas. Analyzing 30,895 English-language tweets, the researchers found, «Adidas has a more positive sentiment than Nike.» However, over 50% of the tweets had a neutral sentiment. Emotional tone analysis can help to get more positive mentions in the market.
3. Context meaning in human resource management. In recruitment, machine learning algorithms streamline the process by sifting through numerous resumes and conducting sentiment analysis on employee surveys. HR agency Fuzu in Helsinki leverages NLP to enhance user onboarding for young East African professionals. Contextual understanding models provide personalized recommendations, utilizing data extracted from uploaded text documents like resumes and cover letters. This implementation resulted in a remarkable 30% increase in the click-through rate for job applications.

• Adjust to the client. Different NLP can be used to analyze the mood of the person calling customer support. Sophisticated algorithms determine when to switch from machine to human customer support.
• Standardize processes with chatbots. Chatbots recognize patterns and sentiments in user feedback, enabling businesses to create standardized responses to common issues. This ensures consistent and efficient interactions as chatbots apply learned insights from customer reviews to address similar concerns in practice.
• Filter spam emails. The programs compare typical words and phrases in spam emails with naturally generated text messages, which helps not to lose them from sight and address such messages properly.

Companies use the insights from sentiment analysis to improve customer service, customer satisfaction, and increase their brand loyalty. Let’s examine three use cases of successful NLP utilization in well-known companies.

1. Customer reviews analysis (e.g., Amazon)

Amazon uses NLP to analyze customer reviews. The platform employs opinion mining to understand the emotional tone of the reviews and identify key themes. This helps in summarizing customer opinions and contributes to user experience enhancement.

For instance, through NLP analysis, Amazon can detect a recurring desire for quicker and more reliable delivery in specific regions, which resulted in the introducing of a new feature like «Amazon Prime Same-Day Delivery». This adjustment addresses customer preferences for faster shipping and enhances the overall product offering.

2. Hotel booking platforms (e.g., Booking.com)

Companies in the travel industry, such as Booking.com, implement NLP to analyze hotel user reviews. NLP algorithms can identify sentiments and conduct feedback insights analysis.

Using the received insights, the platform can establish a «Customer Service Excellence» category, prominently ranking hotels with consistently high praise for service. This allows customers seeking superior service to easily identify and choose hotels that align with their preferences.

3. Social media monitoring (e.g., Hootsuite Insights)

Social media management tools like Hootsuite Insights leverage NLP to analyze user-generated content across various platforms. They help to comprehend public sentiment, such as gauging reactions to their products on social media platforms like Twitter. They also utilize these tools to track brand mentions on various online forums and identify emerging trends by analyzing patterns in customer feedback across different channels.

To make data-driven decisions based on the insights gathered from social media conversations, consider investing in natural language processing as a service. Lightpoint not only develops customer behavior analytics apps but also helps to set up a reliable helpline based on predictive analytics.

NLP Frameworks for Sentiment Analysis

Many professional analysis tools can support you in unstructured data analysis. Check out the following popular NLP frameworks that have already proven successful for various businesses.

1. NLTK (Natural Language Toolkit)

• Enables businesses to perform various NLP tasks such as tokenization, stemming, tagging, and parsing, enhancing text analysis capabilities.
• Facilitates rapid development and experimentation in NLP applications, supporting research and development initiatives.

2. Spacy

• Offers high-performance NLP processing, enabling businesses to process large volumes of text efficiently.
• Provides pre-trained models for various languages, saving time and resources for multilingual application development.

3. Stanford NLP

• Offers a suite of NLP tools for tasks like part-of-speech tagging, named entity recognition, and sentiment analysis, enhancing overall language processing capabilities.
• Provides robust support for multiple languages, catering to businesses with diverse linguistic requirements.

4. BERT (Bidirectional Encoder Representations from Transformers)

• Enhances language understanding by considering textual context from both directions, thus refining customer satisfaction metrics.
• Enables businesses to deploy pre-trained BERT models for tasks like text classification, sentiment analysis, and named entity recognition, accelerating development timelines.

5. GPT (Generative Pre-trained Transformer)

• Facilitates natural language generation, allowing businesses to create human-like text for applications like chatbots, content creation, and automated customer interactions.
• Supports transfer learning, enabling businesses to fine-tune pre-trained models for specific tasks, reducing the need for extensive training datasets.

By using these frameworks, companies can gain valuable insight and be better equipped to meet customer needs and ensure customer satisfaction. The choice depends on individual business needs. 

1. Emotional and сontextual сomplexity

Language, driven by emotions and context, poses challenges for algorithms to detect nuances like sarcasm or hidden criticism — honestly, it’s not always easy for a human, either.

Ambiguous example: In response to a colleague’s tardiness, John remarked, «Nice of you to join us in the 11th hour.» 

Expert tip: Implement trial-and-error principles, allowing software to gradually recognize special contexts and refine algorithmic interpretations, minimizing misinterpretations.

2. Unstructured linguistic data

Linguistic data is inherently unstructured, making it challenging for programs to rely on logical rules.

Ambiguous example: A customer review stating, «The product is kind of okay, I guess,» leaves uncertainty about whether the customer is satisfied or dissatisfied with the product.

Expert tip: Focus on standardization and structure where possible to facilitate easier processing by digital systems, acknowledging the complexity of human language.

3. Rough and loose communication rules

Human communication rules are often rough and loosely structured, complicating the task for NLP in social media to identify and understand these nuances.

Ambiguous example: When asked about the suggestion, the customer replied, «Hmm, that’s one way to do it.» Such a loosely structured response makes it tricky for NLP to figure out what the customer really thinks.

Expert tip: Leverage NLP methodologies to recognize and interpret the inherent complexity of human communication structures, aiming for improved understanding and accuracy.

The key to unlocking the true potential of NLP systems lies in their ability to constantly learn and adapt to social sentiment. In line with this, Lightpoint experts have developed a well-established process for implementing NLP in practice, ensuring continuous improvement and adaptation to evolving linguistic nuances:

1. Data collection: Gather a dataset comprising customer reviews from various sources such as e-commerce platforms, social media, and review websites.
2. Text preprocessing: Clean and preprocess the text data, including tasks like removing irrelevant characters, stemming, and converting text to lowercase for standardization.
3. Tokenization and feature extraction: Tokenize the preprocessed text into individual words or phrases. Extract relevant features, such as word frequencies, to represent the textual data in a format suitable for NLP algorithms.
4. Sentiment analysis: Apply NLP algorithms, like machine learning models or semantic analysis approaches, to analyze the sentiment of each review. Based on the extracted features, classify sentiments as positive, negative, or neutral.
5. Performance evaluation: Evaluate the accuracy and performance of the NLP sentiment analysis model using metrics like precision or recall. If needed, refine parameters for improved accuracy in analyzing future customer reviews.

Not sure if NLP analysis is getting the full job done? Leave it to the Lightpoint experts with extensive data science solutions experience.

Conclusion

Sentiment analysis can be used in various ways and is already irreplaceable in numerous industries. Data from social media channels, rating platforms, and online customer surveys can be automatically read out to create a reliable mood picture.

To conclude, the key function of sentiment analysis for modern businesses is to determine and interpret sentiments expressed in text data, aiding in understanding public opinion for fortifying brand reputation management. In the future, sentiment analysis is likely to evolve towards enhanced contextual understanding, incorporating advanced machine learning techniques for more nuanced and accurate interpretation of diverse and evolving language patterns.

That’s why it’s worth taking an outside look at your data. Contact a Lightpoint expert today to discover how you can gain insights faster from social media and other data across your business online touchpoints.