What Is Sentiment Analysis?
Sentiment analysis is the process by which human opinions, feelings, judgments, viewpoints, and conceptions are analyzed to determine the emotional tone. As a subfield of machine learning, sentiment analysis is how machines are trained to understand the real meaning of human words in speech or writing to discern the emotions expressed. It is also referred to as opinion mining as it involves analyzing large volumes of written and spoken text and trying to discern the opinion expressed therein, identifying and classifying them into different categories such as positive, negative, or neutral.
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Sentiment analysis plays a crucial role in brand management. As companies strive to satisfy their customers, they also need to understand and consider how the consumers feel, think, or view their products. That includes understanding whether the customers are satisfied or dissatisfied with their products, how they feel about the products, and their opinion about what is working or not. Sentiment analysis provides a tool to understand all this by properly analyzing whether the consumers have a positive opinion, viewpoint, or emotion toward the business product. In the age of social media, where consumers first read the views, opinions, and feedback from other consumers online before making a purchase, managing how consumers feel or think about your product becomes crucial to brand success.
Emotion detection focuses on understanding human emotions beyond the usual expression of polarity. That includes understanding the meaning behind expressions and texts of words. Machine learning models can be trained to understand human emotions by learning to understand and interpret human language, including speech and text. Combined with computer vision, NLP AI algorithms are trained to decipher emotions by understanding expressions, words, and emojis meaning when used in different contexts. For instance, intelligent emotion detection systems are trained to understand human expressions based on factors such as eyes, eyebrows and mouth. They then adjust their responses accordingly, making their interactions with humans more natural.
Sentiment classification is the process of identifying the opinions expressed in a text and then labeling the identified as either positive, negative, or neutral based on the context of the expressed opinion. In machine learning, NLP-based AI and ML models are trained to understand human speech and text, the context, and the opinions expressed. The model can then classify the opinion as either positive, negative, or neutral, following different sentiment classification rules. For instance, an AI model could be trained to assign a certain weight when certain words are present in a text. The overall weight is then used to classify the opinion expressed in the text as either positive, negative, or neutral.
Fine-grained sentiment analysis is ubiquitous. Its popularity is because of its simplicity making understanding human sentiment simple. Unlike other sentiment analyses that rely solely on text, the fine-grained analysis includes the ratings or reviews of a customer about a product to determine the sentiment expressed. The method offers a more accurate way to understand human sentiment due to its high level of polarity. For instance, a customer who rates a product a one-star is probably very disappointed or dissatisfied. Together with their review, it is possible to understand the customer sentiment expressed accurately. Machine learning models can then be trained to understand this rating and correctly predict the human sentiment expressed.
The aspect-based analysis focuses on tying specific sentiments to particular attributes or features in the text. Focusing on the specific attributes helps achieve more accurate results as the business can understand the specific aspects associated with different sentiments instead of focusing on the general sentiment. Using properly labeled data, AI and ML models are trained to identify the attributes in a text and associate the sentiment expressed to the appropriate attribute. These models are then used to analyze and predict sentiments expressed about different features, components, or attributes in a product giving companies a more precise picture of how the customers feel about their product.
Types Of Sentiment Analysis Parameters
Human language is complex, and understanding the sentiment expressed therein can be daunting. To understand the sentiment expressed, it is necessary to consider several factors, including the urgency expressed in the text, the emotions, the polarity of the words, and the context in which the words are used.
Polarity refers to the neutrality, positivity, or negativity of the words in a piece of text. When conducting the sentiment analysis, especially for brands, the focus is more on the reviews left by the consumers and whether the reviews are positive, negative, or neutral. When more reviews are negative, the associated sentiment could mean the users are dissatisfied with your brand. More positive reviews mean customers are satisfied with your products, while neutral reviews mean the users are lukewarm. When the analysis shows more neutral reviews, the focus should be to improve the brand to have more positive reviews.
Emotions are natural to humans. They are expressed anywhere, including in speech and written texts. Businesses can conduct sentiment by focusing on detecting the emotions evoked by their product in their consumers. By focusing on emotions, a business can determine whether users are sad, happy, or dissatisfied when using its products. For instance, a person expressing sadness in their text could mean dissatisfaction with the product as whole or certain components. If such emotions are identified early or on time, the business can improve the product to improve consumer sentiment.
Urgency in the context of sentiment analysis refers to focusing analysis on identifying relevant texts to determine how soon you need to find a solution to user problems or crises at hand. Urgency detection focuses on detecting information relevant to the crisis at hand and is therefore understood as a specific version of relevance detection. It involves determining whether the problem can be solved promptly or if they can wait for a later date. For instance, if the sentiment shows that the users are dissatisfied because the product is causing accidents and harm, the solutions need to be immediate.
Human actions and behavior are driven by intentions. Understanding the intent behind an action or behavior can be very helpful in understanding an individual. For businesses, understanding their customers’ intent is very useful as it allows them to understand their next course of action. For instance, companies need to understand prospective customers’ intent to determine whether they intend to buy or use their products. When conducting intent analysis, the user’s text data, including expression, are used to determine user intent. Through intent-based sentiment analysis, businesses can target their marketing efforts effectively.
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Challenges Faced In Sentiment Analysis Services
Human language is expansive. Different words and symbols have different meanings in different societies or when used in different contexts. That makes it hard to train NLP AI models to correctly understand human language and predict the opinion, views, or emotions expressed in a text. Due to the diversity of human language, sentiment analysis faces several challenges, which are often an outlier in normal language use.
Sarcasm involves using positive words or phrases to denote negative and vice versa. That makes it extremely hard to correctly label the sentiment expressed in such a text as it calls for a high level of contextual analysis and language comprehension. In some contexts, sarcasm is hard even for humans to understand and characterize, resulting in wrong analysis. Identifying and comprehending sarcasm by humans makes it hard to train NLP AI models to discern between positive and negative sarcastic texts.
Text obscurity or text ambiguity is a problem resulting from the different semantic meanings of words when used in different contexts.Without proper context, it becomes hard to predict correctly the intended meaning of the words making it impossible to determine the sentiment expressed. For instance, “call me a cab!” could mean the user needs to get a cab or whether the user is asking the other person to refer to them as a cab. Without enough context of the words, it would be to determine the exact sentiment this piece of text expresses.
Emoticon Vs. Emoji
Emoticons and emojis can be used in different contexts to express user sentiment. Understanding what different emoticons and emojis mean in different contexts is hence complicated and can affect the success of the sentiment analysis. Besides, the difference between Eastern and Western emoji in terms of character-level combination means that NLP AI models require extensive and rigorous training, further complicating the sentiment analysis process.
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At Annotation Box, our sentiment analysis services will create high-quality training data sets for your sentiment analysis projects. Our highly experienced team of experts worldwide will accurately determine each word’s context in your raw data, delivering high-quality datasets that guarantee success in training your AI and ML model to analyze customer sentiment correctly. Get in touch with us to learn how our sentiment analysis services can help your brand score big on customer experience.
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