Intro Sentiment analysis is the process of interpreting and classifying emotions (good, bad and neutral) in text data using a combination of Natural Language Processing (NLP) and Machine Learning techniques. Weighted sentiment scores are assigned to topics and categories of text within sentences or phrases. Using this process, data analysts working for large companies are able to measure public opinion, carry out detailed market research, analyze brand reputation, and improve understanding of customer experiences. Be it examining customer feedback in comments or survey responses - with Sentiment Analysis, companies are able to listen considerately to their customers, and modify their products and services accordingly and rapidly. Sentiment Analysis Basics Sentiment analysis of text data is a fairly straightforward procedure. The essential steps involved in this process includes - Breaking down each text document into core components (sentences, paragraphs, parts of speech and tokens) Classifying each sentiment-carrying component and assigning a sentiment score to them (-1 or +1) Intermix scores to get multiple layers of analysis This underlying technology can be easily explained via this example – Let’s analyze these two customer reviews - “Terrible motor and dreadful customer service, total waste of money.” “Bad motor and mediocre customer service, please improve your customer service.” Both sentences talk about similar subjects –a poor product review, clearly humans can comprehend that the first example is way more negative. The adjective-noun combinations that the Sentiment Analysis program will treat as sentiment-carrying phrases in the examples above are: Terrible motor | dreadful customer service | total waste of money Bad motor | mediocre customer service | improve your customer service Humans will draw from their knowledge of adjective-noun combinations to clearly see that the first review was worse than the second one. Computer Sentiment Analysis operates in a similar fashion. Types of Sentiment Analysis (SA) To rate reviews like the two ‘customer reviews’ in the example above, various Sentiment Analysis models have been developed by experts. There are models designed on polarity (good, bad, neutral) and those that distinguish feelings and sentiments (annoyed, content, dejected, etc.), or even models that recognize customer intent (e.g. interested in buying vs. not interested in buying). Some of the most commonly used sentiment analysis models include - Fine-grained Sentiment Analysis Emotion detection Aspect-based Sentiment Analysis Multilingual Sentiment analysis How Does Sentiment Analysis Work? Sentiment analysis employs numerous Natural Language Processing (NLP) techniques and algorithms. The three types of algorithms mainly being used presently include - Rule-based systems that carry out sentiment analysis based on an array of manually created guidelines. Automatic systems that depend on ML techniques to acquire patterns and regularities from a constant flow of data. Hybrid systems that amalgamate both these techniques. Rule-based Approaches Typically, a rule-based approach consists of an array of manually designed rules to help classify subjectivity and the polarities of opinion in the text. Using commonly applied tools in computational linguistics, such as – tokenizing certain noun-adjective combinations, tagging different parts of speech and deconstructing the text. For instance - The algorithm will analyze the text and from it create two separate lists of polarized words and phrases. One list will contain negative words/phrases (e.g. ‘poor’, ‘awful customer service’, ‘missing parts’, etc.) while the other will contain positive words/phrases (e.g. ‘great buy’, ‘loved the product’, ‘great’) The algorithm will then compare the number of negative and positive words and based on which set supersedes the other; the system will assign sentiment to that text. If the numbers are equal, the algorithm will declare the text to have a neutral sentiment. Rule-based systems are extremely unsophisticated and require constant human attention. Automatic Systems Automatic systems don’t need manual rules or attention. Based on ML techniques, in this approach, a sentiment analysis task is demonstrated as a classification challenge, in which a classifier is provided with a text and the ML program automatically returns a classification, such as positive/negative. Deep Learning is a new, diverse and highly advanced set of algorithms that try to impersonate the neural networks in the human brain, creating artificial neural networks to learn and extract information from big data. Although Deep Learning is still in its early stages, it is expected to play a crucial part in Sentiment Analysis in the future. Hybrid Approaches Hybrid systems combine the suitable components of both rule-based and automatic approaches into one arrangement. The results in hybrid approaches are much more accurate. Sentiment Analysis Applications Using Sentiment Analysis, companies can massively improve various facets of their product and service management. Some of these applications include - Social Media Analysis – Companies can gain a deep understanding of all customer comments, reviews, etc. on their social media channels. They can either automate any of their processes or automatically route these comments and mentions to members of their marketing team to respond in the best way possible. Brand Monitoring - Identify how their brand status advances over time. Use these algorithms to research their competition and recognize how their repute also progresses over time. The data can also be used to identify and address potential PR crises aptly and immediately. Customer Feedback - Track buyer sentiment about exact features of the company over time. The analyses can be used to better understand why or how customer feedback improves or worsens over time. The analyses can also be narrowed down and used to target individuals on an individual basis. For instance, if an algorithm is programmed to immediately detect customers who are ‘firmly negative’ towards a product or brand, customer service managers can respond to them immediately. Customer Service – According to a study by Mckinsey and Company, 25% of customers immediately switch brands when they face a customer service issue. Clearly, this is one aspect of business that all companies can improve in. Sentiment analysis can be used to automatically classify text-based customer feedback/queries and instantly detect displeased customers. The program can then prioritize and route these queries to the customer service team to give instant solutions. Market Research – Lastly, but most importantly, Sentiment Analysis plays a huge role in market research. Sentiment Analysis can be used to instantly analyze product reviews, create daily reports of these reviews and compare this data with how close competitors perform. Whether it is comparing sentiment performances across global markets or analyzing social media posts concerning the industry, brands can benefit massively from incorporating a hybrid Sentiment Analysis system. With this constant flow of feedback and information, analysts can now quantify qualitative information and use this data to gain access to developing markets. For instance, if your Sentiment Analysis algorithm detects that vegetarian restaurant businesses in Paris have been generating poor reviews for over a year, you can use this information as a chance to venture into the vegetarian restaurant industry in Paris. Sentiment Analysis Resources While the benefits of Sentiment Analysis are infinite, the resources are limited, since this field is still in its evolutionary stage. Some important sources of information regarding Sentiment Analysis include - Tutorials - There are countless free sentiment analysis tutorials online, designed to suit people from all types of backgrounds – professional coders, amateurs, marketers with no computing experience, seasoned data analysts, etc. Kaggle, for instance, is a place to find such tutorials. Excel – You can learn the basics of Sentiment Analysis using Microsoft Excel. Books – There are several books on this subject that you can find online. Online Platforms – Online platforms such as RapidMiner are great for learning more about data mining procedures without being a skilled data expert. It offers a responsive UI where users can learn more about running ML models and create visual representations of data analysis workflows. Courses and lectures Conclusion The future of Sentiment Analysis is extremely promising. Companies like Rosoka have already made great strides in Multilingual Sentiment and Salience analysis. They offer comprehensive results that can be applied via business software apps and can also be adapted to suit government needs. Soon enough Sentiment Analysis will become an indispensable tool for all businesses.
