Humans are sentient beings; we experience emotions, sensations, and feelings 90% of the time. Sentiment evaluation is becoming increasingly vital for researchers, businesses, and organizations to grasp customer feedback and discover 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. As an illustration, most individuals today just get onto social media to specific their sentiments in content equivalent to a tweet. Hence, text mining researchers work on social media sentiment evaluation to grasp public opinion, predict trends and improve customer experience.
Let’s discuss sentiment evaluation intimately below.
What’s Sentiment Evaluation?
Natural Language Processing (NLP) technique to investigate textual data, equivalent to customer reviews, to grasp the emotion behind the text and classify it as positive, negative, or neutral known as sentiment evaluation.
The quantity of textual data shared online is large. Greater than 500 million tweets are shared every day with sentiments and opinions. By developing the capability to investigate this high-volume, high-variety, and high-velocity data, organizations could make data-driven decisions.
There are three principal forms of sentiment evaluation:
1. Multimodal Sentiment Evaluation
It’s a variety of sentiment evaluation by which we consider multiple data modes, equivalent to video, audio, and text, to investigate the emotions expressed within the content. Considering visual and auditory cues equivalent to facial expressions, tone of voice gives a broad spectrum of sentiments.
2. Aspect-based Sentiment Evaluation
The aspect-based evaluation involves NLP methods to investigate and extract emotions and opinions related to specific features or features of services. For instance, in a restaurant review, researchers can extract sentiments related to food, service, ambiance, etc.
3. Multilingual Sentiment Evaluation
Each language has a unique grammar, syntax, and vocabulary. The sentiment is expressed in a different way in each language. In multilingual sentiment evaluation, each language is specifically trained to extract the sentiment of the text being analyzed.
What Tools Can You Use for Sentiment Evaluation?
In sentiment evaluation, we gather the information (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 technique of analyzing sentiment, which is as follows:
1. Python Libraries
NLTK (Natural Language Toolkit) is the widely used text processing library for sentiment evaluation. Various other libraries equivalent to Vader (Valence Aware Dictionary and sEntiment Reasoner) and TextBlob are built on top of NLTK.
BERT (Bidirectional Encoder Representations from Transformers) is a robust 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 his or her applications. These tools are customizable, so preprocessing and modeling techniques will be tailored to specific needs. Popular tools are:
IBM Watson NLU is a cloud-based service that assists with text analytics, equivalent to sentiment evaluation. It supports multiple languages and uses deep learning to discover sentiments.
Google’s Natural Language API can perform various NLP tasks. The API uses machine learning and pre-trained models to supply sentiment and magnitude scores.
Applications of Sentiment Evaluation
1. Customer Experience Management (CEM)
Extracting and analyzing customers’ sentiments from feedback and reviews to enhance services known as customer experience management. Put simply, CEM – using sentiment evaluation – 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 Evaluation
About 65% of the world’s population uses social media. Today, we are able to find sentiments and opinions of individuals about any significant event. Researchers can assess public opinion by gathering data about specific events.
For instance, a study was conducted to check what views people in Western countries have about ISIS as in comparison with Eastern countries. The research concluded that folks view ISIS as a threat regardless of where they’re from.
3. Political Evaluation
By analyzing public sentiment on social media, political campaigns can understand their strengths and weaknesses and reply to the problems that matter most to the general public. Furthermore, 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 extremely consistent in predicting elections.
Challenges of Sentiment Evaluation
1. Ambiguity
Ambiguity refers to instances where a word or expression has multiple meanings based on the encircling context. For instance, the word sick can have positive connotations (“That concert was sick”) or negative connotations(“I’m sick”), depending on the context.
2. Sarcasm
Detecting sarcasm in a text will be difficult because individuals with the stimulus can use positive words to specific negative sentiments or vice versa. For instance, the text “Oh great, one other meeting” is usually a sarcastic comment depending on the context.
3. Data Quality
Finding quality domain-specific data with no data privacy and security concerns will be difficult. Scrapping data from social media web sites is at all times a gray zone. Meta filed a lawsuit against two corporations BrandTotal and Unimania, for making scraping extensions for Facebook against Facebook’s terms and policies.
4. Emojis
Emojis are increasingly getting used to specific emotions in conversation on social media apps. However the interpretation of emojis is subjective and context-dependent. Most practitioners remove emojis from the text, which is probably not the most effective option in some instances. Hence, it becomes difficult to investigate the sentiment of the text holistically.
State of Sentiment Evaluation 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 handle the challenge of emojis and sarcasm within the text, respectively. Over time, such techniques will achieve higher accuracy, scalability, and speed.
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