In this post we review different types of sentiment analysis including document-level, sentence-level, aspect-based and contextual sentiment analysis. We also cover how to choose the best Sentiment Analysis API for you. And we end with the potential uses for sentiment analysis, although we get into more specifics in part two of this series. But first, let’s briefly review the definition of sentiment analysis.
What is Sentiment Analysis?
Sentiment analysis (also known as opinion mining) is the process of helping users understand human thoughts and feelings in all types of data. Sentiment analysis tools interpret that general feeling – or sense of an object or a situation – using natural language processing (NLP). To do this, machine learning (ML) algorithms systematically identify, extract, quantify, and study affective states and subjective information.
Sentiment analysis detects underlying positive, negative or neutral sentiment in text, voice, and video conversations.
Businesses can leverage this data for a variety of uses. These include shaping sales and marketing plans, evaluating social media posts, improving crisis management and brand strength, and translating digital PR into tangible actions.
What are the Different Types of Sentiment Analysis?
Document-Level Sentiment Analysis
Document-level Sentiment Analysis reviews text and determines whether it has a positive or negative sentiment. It supports any sentiment-bearing text and determines the overall opinion of the document.
Sentiment analysis at the document level assumes that each document expresses opinions on a single entity.
Sentence-Level Sentiment Analysis
Sentence level sentiment analysis determines if each sentence has expressed an opinion. This level distinguishes the objective sentences expressing factual information and subjective sentences expressing opinions.
This time of sentiment analysis first identifies if the sentence has expressed an opinion or not, and then assesses the polarity of that opinion.
Aspect-Based Sentiment Analysis
Aspect based sentiment analysis refers to categorizing opinions by aspect and identifies the sentiment related to each
First, a system identifies the attitude targets mentioned in a given sentence. This process is known as aspect extraction. Once these aspects are identified, a system determines the attitude associated with each target in a process known as aspect-level sentiment analysis.
Rule-based strategies that leverage predefined text classifiers are a common technique for aspect extraction. A variety of approaches have been developed to understand the relationship between attitude targets and their context.
Contextual-Based Sentiment Analysis
Context based analysis is used to recognize cues and enhance other types of sentiment analysis. Contextual sentiment analysis refers to the way words change their meaning with context. The same word or phrase can be positive, neutral, or negative, depending on other words in the sentence.
Sentiment is also strongly influenced by background knowledge. People do not express commonsense knowledge that they expect anyone to know. Understanding this implicit knowledge is vital.
Topic-Based Sentiment Analysis
Topics are key drivers of conversations. In fact, they’re the most important keywords or phrases used. Topic level sentiment analysis breaks down a message into topic chunks and then assigns a sentiment score to each topic. Sentiment analysis on topics determines whether the topics resulting from the conversation are positive, negative, or neutral.
The topics algorithm provides a framework for the user to calibrate and precisely model the relationship among the concepts and understand the semantics used in conversations. Not all sentiment analysis tools offer topic-based sentiment analysis.
Sarcasm and irony are highly prevalent in everyday conversation, which makes sarcasm analysis a critical area of focus for successful sentiment analysis systems.
Research into sarcasm analysis has historically focused on sentence-level understanding. A variety of approaches have been tested focusing on sentence-level features, such as detecting incongruity between the sentiment expressed by different words within a sentence.
Bias in Sentiment Analysis
Whether it’s used in customer care, market research, or reputation management, sentiment analysis typically handles data from a wide variety of demographic backgrounds. With that in mind, it’s critical to remove bias that can introduce error into sentiment analysis.
Bias is frequently introduced into sentiment analysis systems through word embeddings. That refers the underlying representation that results when words and phrases are mapped to a vector space for use by sentiment analysis systems.
Since bias can be easily introduced into sentiment analysis systems, identifying effective de-biasing methods is an emerging area of study. This is an important focus for future development for sentiment analysis.
How to Choose the Best Sentiment Analysis API
When looking for the right API, check for contextual understanding to get the most accurate insights. Also look for how the sentiment analysis is built for audio or video content rather than generic text content. The right sentiment analysis API should provide the performance and extensibility you need to achieve your business objectives. It should also give you the ability to surface useful information in real-time.
Symbl.ai uses deep learning models for sentiment analysis. It’s created on top of existing language models with existing language data to detect sentiments at the sentence level. It works in conjunction with our topic modeling system to scope the segments of conversation by contextual coherence of that segment and assign that sentiment to the related topic.
We take the overall sentiment and calculates it based on topic. Symbl can generate topic level sentiment, offering contextual relevance of the sentiment as well as context of how the topic is set in the conversation. And given our advanced method for sentence level and topic level analysis, you can derive very accurate document level analysis. This sets Symbl apart from other document level analysis tools.
How Does Symbl’s Sentiment Analysis Work?
Symbl uses the BART (Bidirectional Auto-Regressive Transformers) deep learning architecture for sentiment analysis. Our sentiment analysis architecture is first fine tuned on conversational data. From the multi-modal aspect, we use topics to detect scopes in which the said topic is being talked about. We also consider the sentiment of these specific paragraphs in order to compute the overall sentiment of the conversation.
To analyze Sentiment, Symbl.ai combines the Conversation API’s Speech-to-Text messages (usually sentences) with Conversation Topics. For a given conversation, the Topics algorithm analyzes each message and provides a sentiment intensity / polarity score (-1.0 to +1.0) and suggested type (positive, neutral, negative).
Why Use the Symbl.ai Platform for Sentiment Analysis?
Symbl.ai’s Sentiment API offers aspect-based sentiment analysis performed on real-time messages. It also offers polarity values that you can freely define and adjust after testing.
Most importantly, users have access to integrations that bring a human-level understanding to different contexts without upfront training data or custom classifiers. And they get access to other valuable conversation analytics, including speaker ratio, talk time, silence, pace, and overlap.
Symbl.ai’s APIs can be used on both asynchronous audio, video or text data as well as streaming audio or video content. Symbl generates real-time sentiments over WebSocket protocol using Symbl’s Streaming API. You can also get sentiment analysis on recorded conversations by processing using Async APIs and extracting the sentiments on sentences and topics using Conversation API.
For step-by-step instructions on how to implement Symbl.ai Sentiment Analysis visit our docs page.
What Can You Do with Sentiment Analysis?
- Social audio content listening – in day-to-day monitoring, or around a specific event such as a product launch.
- Analyzing video survey responses for a large-scale research program.
- Processing employee feedback in a large organization through meetings.
- Identifying very unhappy customers so you can offer closed-loop follow up.
- See where sentiment trends are clustered in particular groups or regions.
- Competitor research – checking your approval levels against comparable business.
- Use for compliance, risk management and data governance.
- To measure engagement and empathy in internal and external conversations.
Symbl Trackers in conjunction with sentence level sentiment also offer a very powerful and flexible way to do zero shot analysis. This gives the user the power to define the aspect/feature and then perform the analysis.
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