Sentiment analysis is the mining of unstructured text data to extract, classify, and understand the feelings, opinions, or meanings expressed by customers, employees, or other stakeholders. Sentiment mining further detects if the expressed feelings and thoughts are positive or negative. Businesses use this emotion analysis to understand the overall customer sentiment towards their brand, product, or service.
Repustate's sentiment analysis API can perform data mining to extract emotional insights from social media channels, videos, podcasts, customer calls, news, surveys, blogs, forums, or any of your company data.
In the modern world, the customer is more vocal about their experience than ever before. Consumers have moved out of the enclosure of filling forms or writing in forums about their experience. Now they have moved into the space of direct digital communication with brands. Customers use all kinds of social media channels to share their experiences and create a ripple in other consumers' perceptions. This is where our AI-driven semantic mining comes into play; we help you assess sentiment related to your business aspects while your consumers are sharing their experience with the world. It helps businesses explore new opportunities and manage their reputation.
Sentiment mining for emotion analysis determines if a chunk of text is positive, negative or neutral.
Natural language processing and machine learning techniques are used for named entity recognition to extract entities and assign sentiment scores to the themes, aspects, and topics within a phrase.
Sentiment mining uses techniques such as part of speech tagging, lemmatization, prior polarity, negations, ampliﬁers & other grammatical constructs, and semantic clustering to assign sentiment scores to social media posts, research survey questions, and documents such as -1 (true negative) and 1 (true positive). A score of 0 or very close to 0 (±0.05) can be interpreted as neutral, i.e., either there was no sentiment expressed or it was ambiguous.
To conduct semantic analysis in multiple languages, we use a combination of Machine learning and Natural language processing. Below are the steps used in processing different languages for emotion mining:
Step 1: Part of speech tagging
Speech tagging involves classifying each word at a grammatical level to identify conjunctions, subordinate clauses, prepositional, and noun phrases in a language. All this helps our semantic mining tool in understanding the true meaning of the text.
Step 2: Lemmatization
Lemmatization applies the rules of conjugating nouns and verbs based on a number, gender, tense, etc., which differ wildly from language to language. It assists our sentiment analysis tool in determining the root of the word. For example, “loved,” “loving,” “lover” are all based on the root word “love”.
Step 3: Prior Polarity
Prior polarity determines the positive and negative context of the word. Of course, our emotion mining tool not just determines the polarity; it also calculated the intensity of the polarity. For example excellent(+1), great(+0.8) good(+0.5), average(0), lacking(-0.25), poor(-0.5), disgusting(-1), etc.
Step 4: Negations, amplifiers & other grammatical constructs
The next step is to lay down the nuanced grammatical aspects unique to each language, including negations and amplifiers. For example in English; we can assign sentiment scores like good(+0.5), very good(+0.75), not good(-0.4) etc. But in some languages, negations and amplifiers come after the phrase, and our multilingual NLP models can determine it all.
Step 5: Wrapping it all up using machine learning
Finally, all the sentiment scores are fed to our machine learning models that combine all the factors of prior polarity, lemmatization, grammatical constructs with language dialects, local idioms, puns, etc. Machine learning then generates sentiment scores at the document, topic, and aspect level.
Sentiment can be expressed in a word, a phrase, or a sentence. But each word or phrase can deliver different emotions when looked at in a large context. When analyzing sentiment, our tool goes beyond prior polarity and looks at the context of the whole sentence. We use a plethora of machine learning and NLP models to sense emotional aspects like the intensity of polarity(excellent, good, average, poor, disgusting, etc.), negators(wasn't bad, could be better, etc.); Puns; Emoticons; and overall intent(taste was good but not worth it, etc.).
Based on the complexity of the text, we can use different sentiment mining methodologies. Here are the three main way of analyzing sentiment:
Document-level sentiment analysis:
It is the high-level sentiment score generated by evaluating the full context of the statement.
This methodology helps derive sentiment related to different themes being discussed in a statement.
This is the most granular method of analyzing sentiment that can derive insights related to each entity in a statement.
Repustate is able to achieve high levels of accurate emotion analysis due in part to the large and varied amounts of training data used to create our sentiment models. Data from news, social media, product reviews and more have been used to create an NLP based comprehensive and wide-reaching language model that accurately captures how people speak. By leveraging pre-trained machine learned models, Repustate provides you with the world's best sentiment analysis, yielding fast, accurate results in real-time.
Repustate provides you with a true multilingual solution for analyzing sentiment that natively supports over 23 languages - and counting! Using machine-learned models for each language, the tool never translates text to an intermediary language first, ensuring meaning isn't lost in translation. Languages supported include: Arabic, Chinese, Danish, Dutch, Finnish, French, German, Hebrew, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Russian, Spanish, Swedish, Turkish, Thai, Urdu, Vietnamese, and English.
Natural language is complex, fluid, and always changing. To meet this challenge, Repustate's sentiment analytics model is trained on a wide range of text samples, capturing native language idioms, expressions, and turns of phrases. But the emotion analytics model that Repustate uses is not static - it is constantly trained and re-trained, encompassing changes in local dialogue, industry jargon, and common vernacular. Every update of Repustate's NLP-based model for analyzing sentiment is an improvement on the last to ensure that you get the most accurate and comprehensive results while analyzing sentiments from your data.
Repustate's AI based sentiment analysis is available via a simple cloud API for quick and easy integration into your existing tech stack. But if privacy and throughput are what you're looking for, an on-premise, cross platform install is also available. With a one click installer and seamless upgrade process, your devops team will have no problem getting up and running with Repustate's sentiment analytics API.
Sentiment analysis, while having many applications, cannot be applied in a “one-size-fits-all”. Language is nuanced – from industry to industry, from data source to data source. Repustate's approach to emotion mining is to cater to the needs of individual customers, allowing them to tweak and customize our emotion analysis engine and sentiment analysis dashboard to best fit their goals.
No data or customer information sent to Repustate is ever stored on Repustate servers or shared with any other 3rd party. Repustate employees are forbidden from looking at any transient data passed to the Repustate API unless explicitly asked to do so by a customer.
With the ever-increasing amount of data being generated, the ability to quickly analyze and extract useful emotional insights from from data, regardless of source, is paramount to any good software product. Repustate's sentiment analytics API regularly handles billions of API calls per day, allowing developers to process API requests in parallel and in bulk to improve throughput.
Your dedicated engineer is always available for support calls. That means no time waiting on hold – no filing tickets and waiting for a response. Our client success team will work with you to plan, measure and report on partnership metrics that ensure you exceed your goals on an on-going basis. Repustate's success objective is that every client optimizes the strategic value of their data with text analytics and apply the results to meet real-time business challenges.