To analyze sentiment means to detect if the feelings and thoughts in the language used for communication are positive or negative. For analyzing sentiment, unstructured text data is processed to extract, classify, and understand the feelings, opinions, or meanings expressed across hundreds of platforms. Repustate's sentiment analysis API does so and more. It performs data mining to extract emotional insights from social media channels, videos, podcasts, customer calls, news, surveys, blogs, forums, or any of your other company data, whatever the format.
Businesses use this emotion analysis to understand overall customer sentiment towards their brand, product, or service.
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The sentiment analysis algorithm determines if a chunk of text is positive, negative or neutral. It uses natural language processing (NLP) techniques such as part-of-speech tagging, lemmatization, prior polarity, negations, and semantic clustering.
Named entity extraction is used to identify the key topics or entities that appear in the text
Next, sentiment scores are assigned to each of the extracted topics, entities and aspects for analyzing sentiment. The scores can range from -1 (true negative) to 1 (true positive) to the comment/text. A score of 0 or very close to 0 (±0.05) can be interpreted as neutral, i.e., either there was no sentiment or emotion expressed, or it was ambiguous.
To conduct multilingual sentiment analysis, the software uses a combination of machine learning (ML) and natural language processing (NLP). Below are the steps used:
Step 1: Part of speech tagging
Speech tagging involves classifying each word at a grammatical level to identify conjunctions, subordinate clauses, and noun phrases in a language. All this helps our sentiment mining tool understand the true meaning of the text.
Step 2: Lemmatization
Lemmatization applies the rules of conjugating nouns and verbs based on a number, gender, tense, and such, which differ widely from language to language. It assists in analyzing sentiment by helping the engine determine the root of a 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 calculates 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 grammatical aspects unique to each language, including negations and amplifiers. For example, in English, we can assign scores to analyze sentiment such as “good” (+0.5), “very good” (+0.75), and “not good” (-0.4). But in some languages, negations and amplifiers come after the phrase, and our multilingual NLP models can determine them.
Step 5: Wrapping It All Up Using Machine Learning
Finally, all the sentiment scores are fed to our ML models. This combines all the factors of prior polarity, lemmatization, grammatical constructs with language dialects, local idioms, puns, and such. Machine learning then generates sentiment scores at the document, topic, and aspect level.
A model trained as above can be used for voice of the customer (VoC) analysis, patient voice insights, understanding surveys and reviews in any of the 23 languages that Repustate offers owing to the fact that each language has its own part of speech tagger and lemmatizer that has been manually created and curated by our data scientists over the years.
Want to know more ? Learn everything about the multilingual sentiment analysis process.
Based on the complexity of the text, we can use three types of sentiment analysis methodologies as listed below:
Document-level sentiment analysis:
It is the high-level sentiment score generated by evaluating the full context of the statement.
Topic-based sentiment analysis:
This methodology helps analyze sentiment related to different topics & themes being discussed in a statement.
Aspect-based sentiment analysis:
This monitoring approach is the most detailed and gives results by analyzing sentiment at a very granular level. It constitutes aspect-based sentiment analysis of each aspect identified in the text.
Every business is unique. Learn which approach to analyze sentiment will suit you best.
Our sentiment analysis tool is faster and more accurate because it is based on a massive corpus of training data. With 6 million entities and 300 classifications, its advanced Named Entity Recognition accurately analyses details across all sources.
Repustate natively supports over 23 languages and dialects including Arabic, Mandarin, and Korean. The Repustate multilingual sentiment analysis API never translates text to an intermediary language, thus leading to greater accuracy in analyzing sentiment.
Repustate’s sentiment analysis software is trained on a wide range of text samples, capturing native language idioms, industry jargon, and expressions. Every update of its natural language processing algorithm is an improvement on the last, so you can get the strategic value out of the text analytics API for meeting real-time business challenges.
Repustate’s sentiment analysis tool is available as a cloud API for quick and easy integration, or as an on-premise installation. With a one-click installer and a seamless upgrade process, you can get up and running with the solution quickly.
The Repustate sentiment analysis dashboard gives you a visual representation of your sentiment analysis results. Insights are presented in the form of charts, graphs and statistical bars, and making them easier to understand.
No data or customer information sent to Repustate is ever stored on Repustate servers or shared with a 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.
Repustate’s sentiment analysis API regularly handles billions of API calls per day. It allows developers to process API requests in parallel and in bulk to improve throughput.
Our client success team works with clients to plan, measure and report on partnership metrics on an on-going basis. Your dedicated engineer is always available for support calls. This translates to means no time waiting on hold – no filing tickets and awaiting a response.
There are many sources from which content can be used for analyzing sentiment. They include news, public information, social media, customer reviews, customer service call centre data, employee interaction data, electronic health records, and more. Here are a few examples:
SInformation can be gathered for social media listening from not only comments and tweets, but also user-generated videos. Sources include all social media platforms such as TikTok, YouTube, Instagram Live, Twitter, Tumblr, WeChat, Reddit, Qzone, Youku, and others.
Customer reviews found on websites like Expedia, Travelocity, TripAdvisor, Yelp, Yahoo! Local, Demandforce, Superpages, Xbiao and others like them are all excellent sources.
This is an important source for all companies, especially in the financial sector. Stock market news is perhaps the most highly anticipated source of information in an industry where political unrest, financial reports, oil market reports, all have a significant impact.
A company’s brand reputation is heavily influenced by any public news and information related to its investors, executives, labour policies, or political alliances. Sentiment monitoring of public opinion can be of great help to a company’s PR department.
Employee Interaction Data
Employee feedback programs are a vital source of information to companies. Details can be collected through audio platforms, chatbots, videos, HR interactions, intranet chat, and employee surveys.
Electronic Health Records
EMRs are an important source of information for measuring patient experience. Apart from these, medical review websites like SeniorAdvisor, RateMDs, RealSelf, Healthgrades, and such are also great sources.
The best tool for analyzing sentiment should have certain sentiment analysis features that power it to conduct fine-grained sentiment analysis of emotions in multiple languages. These features are:
So the API can pick up nuances of the language.
Inbuilt part-of-speech taggers ensure that the meaning is not lost in translation.
Speed & Scale:
This is crucial in time-sensitive projects like stock sentiment analysis or brand protection.
So that it can identify millions of entities through its named entity recognition capability
This means it is completely aligned with your industry vertical.
So you have a tangible view of past and present insights to plan strategies.
Social Media Aptitude:
So it can recognize social media slang, industry-specific jargon, hashtags, emojis, and emoticons.
So the API can discover and analyze sentiment from forms of communication that includes Audio, Video, Image & Text.
Helps it to recognize names of people, businesses, brands, products, locations, etc..
To give you a choice between an on-premise installation and a cloud API.
All the above are vital for analyzing sentiment and must be present in the sentiment analysis API you choose for yourself. Get all the details here.