There is growing demand to capture customers’ emotions and feelings, including positive and negative reactions. Analyzing these reactions, and extracting intelligence from them, can help a brand gauge the ROI of its PR and marketing efforts. This digital era has become far more comfortable for organizations to collect opinions and feedback from people about brands, products, and services. Not that long ago, the process of gathering views and feedback was accomplished by word-of-mouth and obtained from sources like family, friends, and acquaintances, while later from broader audiences through hardcopy forms, postcards, or other documents filled out in a handwritten form.
With the advent of social media, it has become much easier to collect diverse opinions from broad spectrums worldwide. Today, many internet sources gather views on how a particular product or service may be perceived in the market. Ecommerce sites like Amazon, eBay, Etsy, Wayfair, Overstock, Alibaba, Flipkart, Trip Advisor, and Rotten Tomatoes thrive on feedback acquired directly from visitors and customers in the form of product ratings, reviews, or opinions left online, in digital format, evaluating products, specific orders, or services provided.
Suppose a particular organization captures these brand-specific raw opinions while employing advanced analytical technologies. In that case, it is both possible and practical to harness, detect, and classify the emotional tone behind a particular opinion, giving rise to key and deeper insights into customer satisfaction.
Sentiment analysis is the process of identifying and categorizing these opinions expressed within the text – whether content on social media, websites, within articles, reports, journals, reviews, etc., – into positive, neutral and negative or into more granular levels, like very negative, negative, neutral, positive, very positive. A sentiment analysis system combines natural language processing (NLP) and machine learning techniques to allocate sentiment values to topics, themes, and categories within sentences.
Machine learning for sentiment analysis
Machine learning improves and automates the low-level text analytics functions on which the entire sentiment analysis relies. It helps solve the tricky issues of inconsistencies in natural language. There are various techniques and complex algorithms to train the machines to perform sentiment analysis.
While a logistic regression model trains swiftly on larger datasets and provides robust results, models like Support Vector Machines (SVMs), Random Forests, Naive Bayes, etc., can be trained on individual tokens, bigrams, and trigrams, allowing the classifier to pick up negations and short phrases. With the advent of deep learning, new standards to measure sentiment analysis models have developed. Additional accuracy can be achieved in most advanced models by using pre-trained embeddings such as Word2Vec, GloVe, BERT, or FastText.
A convolutional neural network model performs convolutions particularly well in the words’ embedded feature space within sentences. LSTMs and RNNs are the recurrent network models that are ideal for working with sequential data where they can repeatedly predict sentiment. High-performance sentiment analysis models like dynamic memory networks and neural semantic encoders are developed based on another promising approach called multi-task learning (MTL), a single model trained jointly across multiple tasks to achieve ultimate accuracy in as many domains as possible.
Challenges and complexities in sentiment analysis
There are some challenges that a sentiment analysis needs to address to become a perfect tool. First, it is wise to proceed with a large sample set of data, as customers’ sentiments are influenced by many psychological, geographical, and emotional factors.
Second, like many NLP techniques, sentiment analysis also needs to cope with the language’s complexities. Automated systems find it challenging to extract the true sentiment from negations and understand different degrees of emotions and conflicting ideas. They also have difficulty differentiating between sarcasm and genuine text, whereas abbreviations and emojis also pose interpretation hurdles.
Hence, many experts prefer a hybrid approach to perform sentiment analysis to combine machine learning with traditional rules and overcome each approach/model’s deficiencies. A rules-based analysis may be an effective path for sentiment analysis. However, with the unmanageable growth in the rulesets, machine learning needs to be brought in to shoulder the complex NLP tasks load.
Sentiment analysis for brand and market research
With the evolution of customers expressing their opinions and thoughts more openly than ever before, sentiment analysis has become vital to monitor and understand customer sentiment. A fundamental sentiment analysis follows the process below:
- – Run data or text extraction from various sources.
- – Run sentiment classifier on each text object extracted to determine if the sentiment is negative, neutral, or positive.
- – Extract aspect terms that determine the sentiment and determine the polarity of each aspect identified.
- – Produce a final summary and group the aspect sentiments together.
How will sentiment analysis benefit business?
- Sentiment analysis helps improve customer service and experience by tracking and monitoring the key messages from customer opinions, thus empowering customer service departments or operational teams to take relevant and swift actions.
- Sentiment analysis provides insightful business intelligence about customer preferences, enabling developing strategies to improve service quality, create better products, reduce customer churn, and enhance product presentation.
- It helps construct a thorough market analysis by discovering the latest trends and new business opportunities. By observing customer opinions on a specific brand and detecting the key messages, new and targeted marketing strategies can be discovered.
- As sentiment analysis captures the moods and impressions of the customers in new and profound ways, it becomes a powerful tool to increase sales revenues.
Thus, sentiment analysis opens the door for organizations to move to the next level beyond social media and brand monitoring, into the realm of strategic platforms for drawing inferences and deeper insights from customer feedback and service, market research, competitive research, product analysis, and workforce analytics. Through understanding your customers better, at just the right time, you too can turn negatives into positives, creating a competitive advantage.
If you would like to learn more about implementing sentiment analysis in your business, send us your query to firstname.lastname@example.org. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with Intellect 2 TM. IntellectDataTM develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellect2.ai.