In recent years, machine learning and artificial intelligence enable enterprises to extract knowledge and insights from large volumes of textual data such as e-mails and social media posts. Most of the respective applications fall in the realm of natural language processing (NLP) i.e., they combine text analysis and computational linguistics to automatically identify the meaning of textual information. Sentiment analysis is one of the most prominent NLP applications for modern enterprises. It leverages text analytics to extract and analyze comprehensive information about the affective states of the subjects that produce the text. For instance, sentiment analysis tools identify whether the nuance of a specific text is positive, negative or neutral. In several cases, they can also grade and quantify the level of positivity or negativity of the text.
As already outlined, sentiment analysis is a machine learning application. As such it is developed based on classical machine learning and knowledge extraction methodologies, which involve the tasks of collecting data, exploring and preprocessing data, testing various models, evaluating alternative machine learning models and ultimately deploying the most successful ones. The machine learning models used in sentiment analysis are primarily aimed at developing an “affective” scoring mechanism. This mechanism helps classifying words, phrases, or entire conversations in terms of their sentiment (e.g., positive or negative). Accordingly, the scoring mechanism is used to classify and analyze text that corresponds to opinions and comments.
There are different ways for scoring phrases or even entire groups of phrases. For instance, leveraging a dictionary of keywords it is possible to identify the sentiment of specific comments based on the keywords that they contain. Specifically, a phrase that comprises many positive keywords (e.g., good, fantastic, spectacular) is likely to reflect positive sentiment. On the other hand, negative keywords (e.g., bad, fail, disappointing) are strong indicators of negative sentiment. Nevertheless, as language evolves and more complex constructs are possible, keyword scoring alone cannot lead to satisfactory accuracy. This is where Machine Learning (ML) models come in. ML models are trained with large volumes of labeled textual data to become able to classify sentiment. Moreover, they are fed with many dictionaries of keywords and are tuned based on domain knowledge provided by linguistic experts. In this way, they achieve acceptable accuracy for business applications.
A variety of ML models are currently used for sentiment analysis. Surprisingly, it is possible to build simple, yet effective sentiment analysis tools using classical ML models like Naive Bayes, Support Vector Machines, Decision Trees, and Random Forests. Nevertheless, these models work well in cases where the training dataset is rather small. As the volumes of training data increase, deep learning techniques (i.e., deep neural networks) yield much better performance. General-purpose Recurrent Neural Networks (RNNs) (e.g., the popular Long Short-Term Memory (LSTM) model) and Convolutional Neural Networks (CNNs) architectures have been successfully used in sentiment analysis problems. Furthermore, more specific deep learning methods have emerged to facilitate NLP and sentiment analysis tasks. As a prominent example, Recursive Neural Tensor Networks have been introduced and proven very effective in capturing complex linguistic patterns. State of the art sentiment analysis tools tend to combine multiple ML models, which helps them outperform conventional techniques. Recently, unsupervised learning approaches for NLP and sentiment analysis have been also proposed (e.g., the Unsupervised Sentiment Neuron from OpenAI). Their main value proposition lies in their ability to operate with very small amounts of training data. This can be a huge advantage in some contexts.
Sentiment analysis tools are nowadays very powerful marketing and branding tools. They are used to monitor sentiment about products, brands, services, and to analyze customer feedback as part of customer analytics and retail analytics processes. Here are some prominent use cases:
In the era of Machine Learning and AI, sentiment analysis is, without doubt, a powerful tool for enterprise growth. Modern enterprises must integrate sentiment analysis in their marketing, branding, and customer relationship management strategies. It is already proven that sentiment analysis improves marketing performance, generates leads and increases customer satisfaction. Therefore, companies must consider how to integrate sentiment analysis insights in their marketing and branding strategies. In this direction, they must analyze the very rich landscape of sentiment analysis tools towards selecting the vendor and services that can best suit their needs.
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