Sentiment analysis is also known as sentiment classification and intent mining; it is a technology that allows machines to identify and understand human emotional language texts. With the development of Internet technology, everyone is inseparable from their mobile phones. Any consumption behavior, life and leisure, food reviews, and travel decisions can be shared and made public through network connections. Merchants also use the information recorded by the Internet to make important business decisions and marketing plans. For example, public opinion monitoring, such as user feedback information, any positive or negative information will affect the purchase trend of consumers, and therefore, merchants will hope to obtain this information in a faster and more effective way to meet their user needs. A machine that can understand people’s minds is like the telepathy and mind control ability of Professor X in the X-Men. It can spy on and grasp intentions from the depths of the human heart, use good products to get good reviews, and receive negative reviews to judge the reasons and Attribution to enhance product iteration and optimization. And all of this is inseparable from the teaching of data to machines, so that machines can understand human emotions and understand human intentions. This article will briefly talk about what sentiment analysis is, how to label sentiment analysis data, and how to obtain sentiment analysis data.
What is Sentiment Analysis?
Sentiment analysis can determine whether a piece of content is positive, negative or neutral by extracting specific words or phrases. The main purpose of sentiment analysis is to analyze the audience's opinion on certain products, events, characters or words. As opposed to objective facts, emotions are subjective expressions used to describe how a person feels about a particular subject or topic. Although "emotion" and "mood" are used interchangeably by many people, there is a fundamental difference between the two concepts. Affect implies a more organized disposition of a goal, while emotion describes an involuntary physiological response. In text, emotion can be expressed in two different ways. It can be explicit, where an opinion is expressed directly (eg: "This dress is beautiful"), or implicit, where the text implies an opinion (eg: "My dress broke last year."). Most sentiment analysis research focuses on explicit emotions because they are easier to detect and analyze. There are usually two aspects to analyzing sentiment:
- Emotional Polarity: Analyzing the direction of emotion. (is it positive or negative?)
- Emotional intensity: high to low emotional level
How to do data annotation for sentiment analysis?
Through the artificial intelligence-based sentiment analysis model, speech data such as text, audio or voice in the video can be understood. NLP labeling, entity labeling, and text labeling are common voice data labeling methods. Through this type of data labeling, machines can be trained to understand human emotions and analyze the emotions of different people in the next judgment.
Recommendations for starting a sentiment analysis tagging project
- Develop project charter and standards
Make text-based sentiment labeling easier. Many sentiment analysis projects involve a large amount of text annotation. Simple and straightforward explicit text like "coffee tastes bad" can require annotators to directly mark "positive", "negative" or neutral; complex implicit text will It is difficult to set a standard. Therefore, when it comes to the expression of complex emotions such as "sarcasm" and "irony", standards are particularly important, which directly affects the project cycle and the quality of data delivery.
To help minimize human error, labeling teams need to undergo rigorous training and assessment. Especially in the case of sentiment analysis, there is often no right or wrong answer, making it difficult to measure accuracy. Metrics like Cohen's kappa (κ), Fleiss' kappa (K), or Krippendorff's alpha to measure agreement between annotators can be used as a measure of quality. These indicators can be used to analyze labeled datasets and labeling standards to improve a series of labeling difficulties encountered in the labeling process.
How to get data for sentiment analysis
The growing need for consumer insights will keep sentiment analysis and opinion mining strongly relevant in the future. This fast-growing technology has the potential to disrupt numerous industries and improve customer experience. Appen is a training data provider in the field of sentiment analysis and content relevance labeling. Appen has been deeply involved in the field of linguistics for decades and has accumulated rich professional experience. Our global crowdsourced resources span 170+ countries and support expertise in 235+ languages. We have helped many companies in retail/e-commerce, finance, insurance, medical care, transportation and other industries successfully implement NLP projects. We provide training data to help build intelligent systems that can understand human text and speech and extract meaning from them, which can be applied to various AI scenarios, such as chatbots, voice assistants, search relevance, sentiment analysis, etc.