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Languages are heavily influenced by historical and cultural factors. They evolve because of political and social reasons. Because of geographical and commerce related factors, some languages develop numerous dialects over centuries, like Chinese. Some languages are related to each other, making them similar, and yet different, like Russian and Polish. From phonetics, to how words are strung together to form a sentence, the accurate and true semantic meaning in a language cannot be gauged by a semantic technology company, by simply translating them into another language, in most cases, English. Case in point, Mandarin has 22 consonants, while Hawaiin has only 8. German is a Category II language, meaning it's a relatively easy language to gain proficiency in by native English speakers, while Korean is a Category V language, which means that native English speakers need more than 2000 hours to achieve the same proficiency.
Chinese differs from English not only phonetically, but also in syntax, word structure and quite simply, vocabulary. When data is directly translated into another language so completely different in etymology, it just cannot give accurate results. The reason being that the language it's being translated into, does not have the vocabulary to match the emotional tone nor the context in which a particular word is used. This is the case for most languages. Which is why this approach of first translating data in one language into English first, to derive semantic understanding, yields terribly inaccurate results. Trying to achieve Chinese sentiment analysis by using an API that uses a translation technique in its algorithm will be of no actual benefit to an organization. That's why Repustate, as a sentiment analysis company, has uniquely developed a sophisticated Chinese sentiment analysis API that extracts meaningful insights from the sea of unstructured data, in the native language itself. Thus giving you a higher accurate representation of the sentiments from the data, and empowering you with a higher degree of focussed, actionable insights.
Unlike English or Latin-based languages, Chinese (simplified) doesn't necessarily disambiguate words using whitespace. For example the following string of symbols is a completely normal sentence in Chinese:
团购分量比较一般，不过肉多，而且是和两个女生，所以基本都能吃饱。 猪手香肠无得讲，的确系一般餐厅做唔出的味道，其他就比较一般啦。 后来和朋友们正价去吃> 了一次，感觉分量比团购多，希望商家以后能一视同仁啦。
(For those who don't read Chinese, this is a review of a restaurant). Now you'll see a few white spaces here & there but there's actually many more words being expressed than there are separated tokens. So how do we know where one word (or idea) begins and the next ends?
We use a technique called conditional random fields which uses probabilistic models to infer what the meaning of a particular glyph (character) is given the glyphs around it. With a large enough pre-tagged corpus of Chinese text, Repustate can achieve almost 100% perfection in identifying the individual words or ideas being expressed in a long chain of Chinese glyphs.
In order to apply state-of-the-art sentiment analysis in Chinese for Chinese companies, Repustate has developed Chinese-specific tools to decipher words, industry jargon, and the feelings and emotional tone of words expressed in texts covering multiple Chinese dialects. The Chinese sentiment analysis API includes an Chinese part of speech tagger, an Chinese lemmatizer, and Chinese-specific sentiment models.
It can turn unstructured data written in Chinese, gathered from innumerable platforms, including social media listening, chatbots, call transcripts, review forums, and survey responses, into powerful reservoirs of intelligence for you. These meticulous, highly accurate insights can guide you in enhancing your value proposition, brand experience, and value delivery so you win massive gains in your business and investments.
Sentiments are feelings based on an opinion that a person has developed due to their experience of a particular situation or event. When they express this experience in words by uploading a video on Youtube or TikTok, or by writing a review on a website or survey, they are giving tangible proof of their feelings. Chinese part-of-speech tagging allows us to focus on where these sentiments may actually be exactly within a block of text. Verbs, nouns, and adjectives provide the cues necessary to determine sentiment, and aid in detailed analysis.
The first step in Chinese Sentiment Analysis, is to create a fast and accurate Chinese part-of-speech tagger, for which data scientists need to have a massive corpus, or collection of texts, of the manually tagged Chinese text. This text can then be fed into a machine-learning based algorithm to create an Chinese part-of-speech tagger. The larger the corpus, and more importantly, the more varied it is, the better the results in creating the Chinese part-of-speech tagger. Which is why engineers at Repustate have created a massive corpus of Chinese text grabbing data from a variety of sources in order to obtain an all-encompassing coverage of all aspects of business and social interactions.
Further granulating the algorithm for deeper accuracy, Repustate uses Chinese Named Entity Recognition with advanced semantic search for enterprises to identify brand and business entities in data. No matter how misspelled a word is, our API will reproduce the name first in native script and so improve the accuracy of a name search, transliteration, and identity verification measures. This in turn, gives high-accuracy ranked results, on the basis of the linguistic, phonetic, and specific cultural variation patterns of the names.
Repustate has developed sentiment language models specific to Chinese to capture the various phrases, idioms, and expressions that help define sentiments. Understanding the various grammatical aspects of the Chinese language that make it unique and very different from English is what allows Repustate's Chinese sentiment analysis to be as fast and as accurate as it is.
People love to express their experience online in the form of product reviews, recommendations, and even tutorials. So be it through Facebook, Twitter, TikTok, YouTube, or Instagram, now you can analyze sentiment in this massive flow of information to understand how your customers perceive you and your competitors. Unlock the power of social media with Repustate's Social media listening solution.
Sentiment analysis from News streams is really easy with Repustate's Sentiment Analysis API. We extract all the sentiment related to your business aspects from the live streams on a sentiment analysis dashboard. This visualization tool can help you gain a quick insight into current and future trends by converting the data into charts, graphs, and tables, so you can understand the data in an easier format and take calculated risks.
Companies dedicate a large number of resources for understanding the voice of the customer by running feedback campaigns in their stores, social forums, mobile apps, and websites. They even get hundreds of reviews on Google, OpenTable, and other review platforms. But they also get overwhelmed by the sheer volume of feedback and the complexity that comes with it. This is where Repustate's Chinese Voice of Customer analysis helps. It goes beyond just document-level positive or negative feedback, by analyzing each business aspect of every single review, in order to help you dig deeper for accurate, trust-worthy insights that you can use.
It has become common practice for people to take to the internet and share their day-to-day experiences. From policy decisions by government offices, to bad customer service, whatever the issue may be, people feel compelled to share this information with the world. This data can be priceless to a well-organized government, or a company looking to improve its brand perception and market share. This information is also very important in the financial business as public sentiment can indicate how bullish or bearish a market may become.
Our Chinese semantic analysis API is optimized to understand and analyze data in standard Chinese language and dialects. By combining sentiment analysis and named entity recognition, we can help companies identify essential business topics to conduct aspect based sentiment analysis for each topic. Our API can be used to analyze the public sentiment about a product, service, government policy opinions, or stock market trends, among other areas. Here are some of the collaborations that Repustate has had with organizations to provide them with world-class sentiment analysis solutions in these fields.
When the prestigious Nahdi Medical Company, based in the KSA, approached Repustate, it's biggest motivation was its need to understand its patients better. They wanted to make the overall system more efficient and sensitive to the patient experience. Having tried many other semantic technology companies, they remained dissatisfied with the speed and accuracy of the results because of the complexity of the language. Repustate's sentiment analysis API enabled them to close this gap. It gathered impactful insights more accurately and faster than others, while semantically identifying various aspects related to doctor-patient interactions and their overall experience at the hospital. Our agile, highly customizable, solution was able to do this because of its unique ability to read language natively. With Repustate, the Kuwait-based healthcare giant is today able to unlock the answers it was searching for and make better business decisions for its future.
When a Hong Kong based financial corporation dealing in the forex market realises that it's not being able to monitor international financial and stock market news accurately and at speed, it's seriously worried about the damage. Since even names of corporations change when the news is in a forgiegn language, the company is missing out on vital information in an industry where decisions need to be made in sub-second frequencies. As an astute sentiment analysis company, Repustate provides them with a highly-precise, customized, stock sentiment analysis solution. Through its multilingual extraction capability, the solution is able to give them insights into market sentiment and tone, based on price movements of securities traded, as well as financial news coverage in all major languages. They are also able to have a real-time dashboard showing market sentiment and share prices for different debt instruments and equities. And so unleashing a whole new area for growth opportunities by projecting and alerting them with precision and in record time.
A public transport agency from Beijing wants to improve its service and brand perception and approached Repustate with the problem. Such a government agency faces many serious challenges in the form of accidents and safety regulations, environmental impacts due to vehicle emissions, traffic congestions, inclusiveness for the disabled, and so many more. They want to not only provide better service by listening to all negative feedback, but also enhance the areas that had a positive impact on the lives of people. They want to have an authentic and detailed understanding of the sentiment of daily commuters using public modes of transport. Repustate's Chinese sentiment analysis API helps them achieve what they want to. Having gathered data from numerous platforms like social media forums and channels, Repustate is able to help them find the true sentiment behind user opinion and so give them the power to decide where they want to focus their efforts.
Repustate's Enterprise Search automatically annotates your Chinese data with semantic information. This includes relevant entities, topics, and entity-specific metadata. Search any and all metadata associated with any given entity that Repustate finds. You can search by market cap or industry type for business, or perform your search by nationality. Over 100 metadata properties can be searched, and all of them can be automatically determined by Repustate's Chinese Enterprise Search.
中文 是一门独特的语言，并从几个方面与英语不同。在实行 中文 情感分析时，如果使用的是用于英语情感分析的技术和语言模型，则将产生极不准确的结果。
因此，Repustate开发了 中文 专用工具来帮助 中文 情感分析，其中包括 中文词类标签器、中文 词性归并器以及必不可少的 中文 专用情感模型。
为了创建一个快速准确的 中文 词类标签器，你必须拥有一个手动标记文字的大型文集。这些 中文 文字会被传送给一个机器学习算法以创建 中文 词类标签。
文集越大、越多样，创建 中文 词类标签器的结果就越好。Repustate通过从多个来源汲取数据来创建大型的 中文 文集，以此确保良好的涵盖范围。
Repustate开发了 中文 专用的情感语言模型以捕捉各种在中文里帮助定义情感的短语、成语和词句。Repustate理解赋予 中文 独特性并使其与英语截然不同的各个语法，因此Repustate的 中文 情感分析非常快速而且准确。