Humans are sentient beings; we experience emotions, sensations, and feelings 90% of the time. Sentiment analysis is becoming increasingly important for researchers, businesses, and organizations to understand customer feedback and identify areas of improvement. It has various applications, yet it faces some challenges too.
Sentiment refers to thoughts, views, and attitudes – held or expressed – motivated by emotions. For instance, most people today just get onto social media to express their sentiments in content such as a tweet. Hence, text mining researchers work on social media sentiment analysis to understand public opinion, predict trends and improve customer experience.
Let’s discuss sentiment analysis in detail below.
What is Sentiment Analysis?
Natural Language Processing (NLP) technique to analyze textual data, such as customer reviews, to understand the emotion behind the text and classify it as positive, negative, or neutral is called sentiment analysis.
The amount of textual data shared online is huge. More than 500 million tweets are shared daily with sentiments and opinions. By developing the capacity to analyze this high-volume, high-variety, and high-velocity data, organizations can make data-driven decisions.
There are three main types of sentiment analysis:
1. Multimodal Sentiment Analysis
It is a type of sentiment analysis in which we consider multiple data modes, such as video, audio, and text, to analyze the emotions expressed in the content. Considering visual and auditory cues such as facial expressions, tone of voice gives a broad spectrum of sentiments.
2. Aspect-based Sentiment Analysis
The aspect-based analysis involves NLP methods to analyze and extract emotions and opinions related to specific aspects or features of products and services. For example, in a restaurant review, researchers can extract sentiments related to food, service, ambiance, etc.
3. Multilingual Sentiment Analysis
Each language has a different grammar, syntax, and vocabulary. The sentiment is expressed differently in each language. In multilingual sentiment analysis, each language is specifically trained to extract the sentiment of the text being analyzed.
What Tools Can You Use for Sentiment Analysis?
In sentiment analysis, we gather the data (customer reviews, social media posts, comments, etc.), preprocess it (remove unwanted text, tokenization, POS tagging, stemming/lemmatization), extract features (converting words to numbers for modeling), and classify the text as either positive, negative or neutral.
Various Python libraries and commercially available tools ease the process of analyzing sentiment, which is as follows:
1. Python Libraries
NLTK (Natural Language Toolkit) is the widely used text processing library for sentiment analysis. Various other libraries such as Vader (Valence Aware Dictionary and sEntiment Reasoner) and TextBlob are built on top of NLTK.
BERT (Bidirectional Encoder Representations from Transformers) is a powerful language representation model that has shown state-of-the-art results on many NLP tasks.
2. Commercially Available Tools
Developers and businesses can use many commercially available tools for their applications. These tools are customizable, so preprocessing and modeling techniques can be tailored to specific needs. Popular tools are:
IBM Watson NLU is a cloud-based service that assists with text analytics, such as sentiment analysis. It supports multiple languages and uses deep learning to identify sentiments.
Google’s Natural Language API can perform various NLP tasks. The API uses machine learning and pre-trained models to provide sentiment and magnitude scores.
Applications of Sentiment Analysis
1. Customer Experience Management (CEM)
Extracting and analyzing customers’ sentiments from feedback and reviews to improve products and services is called customer experience management. Put simply, CEM – using sentiment analysis – can enhance customer satisfaction which in turn increases revenue. And when customers are satisfied, 72% of them will share their experience with others.
2. Social Media Analysis
About 65% of the world’s population uses social media. Today, we can find sentiments and opinions of people about any significant event. Researchers can assess public opinion by gathering data about specific events.
For example, a study was conducted to compare what views people in Western countries have about ISIS as compared to Eastern countries. The research concluded that people view ISIS as a threat irrespective of where they are from.
3. Political Analysis
By analyzing public sentiment on social media, political campaigns can understand their strengths and weaknesses and respond to the issues that matter most to the public. Moreover, researchers can predict election results by analyzing sentiments towards political parties and candidates.
Twitter has a 94% correlation with polling data, meaning that it is highly consistent in predicting elections.
Challenges of Sentiment Analysis
Ambiguity refers to instances where a word or expression has multiple meanings based on the surrounding context. For example, the word sick can have positive connotations (“That concert was sick”) or negative connotations(“I’m sick”), depending on the context.
Detecting sarcasm in a text can be challenging because people with the stimulus can use positive words to express negative sentiments or vice versa. For example, the text “Oh great, another meeting” can be a sarcastic comment depending on the context.
3. Data Quality
Finding quality domain-specific data with no data privacy and security concerns can be challenging. Scrapping data from social media websites is always a grey zone. Meta filed a lawsuit against two companies BrandTotal and Unimania, for making scraping extensions for Facebook against Facebook’s terms and policies.
Emojis are increasingly being used to express emotions in conversation on social media apps. But the interpretation of emojis is subjective and context-dependent. Most practitioners remove emojis from the text, which may not be the best option in some instances. Hence, it becomes difficult to analyze the sentiment of the text holistically.
State of Sentiment Analysis in 2023 & Beyond!
Large language models like BERT and GPT have achieved state-of-the-art results on many NLP tasks. Researchers are using emoji embedding and Multi-Head Self-Attention Architecture to address the challenge of emojis and sarcasm in the text, respectively. Over time, such techniques will achieve better accuracy, scalability, and speed.
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