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Predicting the Future Based on Voice of the Customer (VoC)
Hitachi's Sentiment Analysis Service provides
a highly accurate visual representation of customers' opinions and sentiments
about a company or a product, based on analyses of text data.
The service can analyze the data from a variety of media, such as social media,
customer reviews, mass media including newspapers and television,
and business data from questionnaires and call centers.
And then supporting future planning activities like sales forecasting.
This function classifies the results into three major emotional categories (positive, negative, and neutral). The text data can then be further broken down into more detailed sentiments (joy, happiness, love, etc.). This function can also classify text into different categories (related to vehicles, food, sports, etc.).
Supported languages: English, Japanese, Thai, Chinese, etc.
This function uses the academically backed Moral Foundations Dictionary* to quantitatively analyze and visualize text data and to categorize keywords according to the following five moral foundations: care, fairness, ingroup, authority, and purity.
Supported languages: English, Japanese
This function uses Hitachi's original algorithm to identify unexpected, previously overlooked information (such the minority response and as small changes in response) that was buried in huge amounts of data.
Supported languages: English, Japanese
By combining the Sentiment Analysis, Moral Analysis, and Insight Analysis functions, you can visualize, quantitatively and from various angles, both the majority and minority response from among huge amounts of data (such as data from social media). This allows you to understand values and hidden needs, so you can improve your existing businesses or get ideas for new businesses.
To use sentiment data for your business, you must be able to quickly and easily find the data you want. To achieve this, this service's dashboard has three features to help you notice important points. First, the dashboard displays analysis data in various graphs (pie chart, timeline, word ranking, etc.). Second, the dashboard can filter analysis data not only by keywords and dates, but also by emotions and topics categorized by AI. Finally, the dashboard can also filter analysis data by tags set by users.
Through the machine learning of highly relevant words and technical terms contained in collected data, the AI automatically updates the refining conditions used for the filtering dictionary. This makes it possible to maintain and improve the accuracy of search refinement without additional maintenance work.
Hitachi's Sentiment Analysis Service is available in a default service package or customized systems to meet your needs.
Data from Twitter and Pantip (Thai only) is supported for analysis in the default plan.
Contribute to Your Business with a Range of Approaches
Product planning to comprehensively meet market needs
Perform sentiment analysis of data that includes both business data, such as product functions and performance, and information from social media. Based on customer complaints about the product, this analysis clarifies areas in need of improvement, which allows you to identify customer needs. This, in turn, enables you to perform product planning in a way that comprehensively meets market needs, thereby contributing to your efforts to increase sales and reduce lost opportunities.
Implementation of prompt measures against risks, such as social media flaming and recall
Perform sentiment analysis of data that includes both business data (such as product specifications and information about the state of business when the product was released) and information from social media. This enables you to detect negative feelings about the product before they spread, and to identify words that indicate risks. This helps you to improve your brand image by promptly implementing measures against the risk, such as preventing accidents that may be caused by defects in the product or announcing a recall.
Effective promotional activities
Perform sentiment analysis of data to monitor customers opinion from social media. Based on the customers opinion about your promotion, you can make an effective improvement for your promotion. Effective promotion activities increases your social media account followers in your business. This will make your account more attractive.
Detection and fast handling of flaming incidents
(Sentiment Analysis × Moral Analysis × Insight Analysis)
By using the Sentiment Analysis and Moral Analysis functions to analyze response on social media and the web, you can swiftly detect signs of flaming. You can then identify the causes of flaming by gaining a quantitative understanding of the moral values that led to the flaming. Furthermore, the Insight Analysis function can be used to detect potential causes for flaming before an incident occurs and to check the minority response. By using these three types of analyses, you can address risks swiftly and properly, thereby maintaining and improving your corporate brand.