User generated content (UGC) plays a key role in global business, localization and marketing strategies. A growing number of consumers post and share comments and reviews about products, services and brand experiences. Many global brands have realized how valuable it is to harness the power and knowledge of their users and encourage conversations, discussions and opinion-sharing. Global companies like TripAdvisor, eBay, Facebook and YouTube are based on business models that share and rank user opinions.
TripAdvisor, the world’s largest travel site, process over 320 million reviews per month! UGC is often posted in more than one language and a growing area in the localization industry is translating and understanding UGC to monitor what multilingual consumers are saying about their brand and products. This is called social listening.
By gathering and understanding UGC, businesses can use this data to promote further online sales, develop online digital marketing campaigns and provide feedback to product development.
FACT: 25% of Search Results for the world’s 20 largest brands are links to user-generated content. Source: Kissmetrics
One tweet or review can contain facts, tone and opinion that can have an impact on how others see a particular brand. It can be a challenge to collectively make sense, rank and monitor UGC data in the source language, not to mention translate UGC into other languages.
Global organizations often use machine translation (MT), to translate UGC and social media content. MT allows large volumes of data to be translated rapidly to a quality level that is acceptable for this type of content. Once UGC has gone through MT, it is often re-published automatically. As part of this localization and translation process, a growing number of organizations are embracing sentiment analysis as a value-added task to rank source and translated UGC.
Sentiment analysis (SA) is the process of computationally identifying and categorizing opinion expressed in UGC, such as product reviews, social media posts and comments. It provides analysis of the “sentiment” of UGC content, to identify whether it is positive, negative or neutral. On a more complex level, some sentiment analysis tools will break down sections of a review, positive or negative, providing an overall outcome or rating for the piece of text.
The technology behind sentiment analysis is natural language processing (NLP) which focuses on the interaction between computers and language to enable text analysis. As organizations generate huge amounts of online UGC data, sentiment analysis is a key tool to make sense and create valuable business knowledge and intelligence. Working as part of an enterprise MT program, sentiment analysis can assess translated UGC text to enable ranking of multilingual reviews.
Global brands can use sentiment analysis as part of the decision-making process, to decide whether to re-publish and keep certain reviews or UGC data live. Data collected can also be used to help assess the performance of a particular product or service by monitoring overall user feedback posted in social media forums.
Integrating sentiment analysis into an enterprise MT program is an effective way to manage and understand large volumes of UGC in more than one language. Welocalize has recently partnered with an innovative NLP specialist and is now delivering sentiment analysis and other text analytics services for a range of languages. For more information about sentiment analysis and Welocalize weMT and language tools solutions, email firstname.lastname@example.org
Based in the United States, Elaine O’Curran is MT Program Manager at Welocalize.
Read more about TripAdvisor and Welocalize partnering together in this case study: https://www.welocalize.com/wp-content/uploads/2013/01/TripAdvisor-Case-Study-2016-.pdf