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Comparing Machine Translation to Native Language Analysis

As our world becomes increasingly global, so does our data. Being an analyst working with almost any size company today often means facing the challenge of receiving text data that contains multiple languages. So what do you do? Essentially, there are two options we may consider: machine translation or native language analysis. With machine translation, we actually create a new dataset where the text has all been translated into a…
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Leveraging online product reviews

In 2018, US customers spent over $500 billion on online shopping. Often, customers write reviews to describe their experience and sentiments about the products they purchased. Since the reviews are publicly available to the world, they naturally play an important role in influencing potential customers’ purchase decisions, steering them towards or away from buying certain products. Online product reviews represent a unique source of unbiased information: customers are sharing their opinions…
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Beyond keyword searching

When we begin a search on the internet, we often use a simple keyword to find exactly what we need. With specified information captured, there are several backend components that feed into the end result. Naturally, we take for granted this instantaneous retrieval. What goes on behind that search contributes to its relevancy, such as the context, the scope, and whether to include information adjacently related to the keyword. Similar…
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Data cleansing with unstructured data

Data cleansing is critical in data analysis. The quality of data cleansing has a direct impact on the accuracy of the derived models and conclusions. In practice, data cleaning typically accounts for between 50% and 80% of the analysis process. Traditional data cleansing methods are mainly used to process structured data, including the completion of missing data, modification of format and content errors, and removal of unwanted data. Resources on…
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