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Case Study: Media & News

The text mining techniques allows to capture and understand people thoughts, opinions and preferences on social networks and web with respect to a certain discussed topic.

In particular, a vertical analysis focuses on a specific market sector of interest, on a certain brand and its competitors, or on desired products of that industry. All the tools are provided to understand the business deeper from a web perspective.

Project Details

Sector: Media & News
Period of analysis: 1 January - 15 February
Total analysed mentions: 16.000
Sources: Facebook

Sentiment Analysis

By means of the Sentiment Analysis it is possible to interpret peolpe feelings from complex textual data. This way the sentiment is translated to quantitative information that generate useful business insights.

The General View shows the estimated aggregate level of sentiment of the whole period of analysis. Here the vast majority of documents were classified as negative, implying a very bad perception, on average on Facebook, about the topic.

The Daily View instead allows to see how the sentiment varies with time, revealing possible interesting trends.

It is usually of interest to analyse deeper the different sentiment paths using a qualitative peak-and-trough analysis. This specific analysis helps to reveal the underlying reasons related to positive and negative opinions.

Topic Discovery

By means of the Topic Discovery analysis it is possible to understand the most discussed topics of some complex textual data. This allows also to capture the underlying justification to the sentiment and NPS evolutions over time, and to perform various types of cross-analysis.

The General View shows the estimated aggregate percentage of discussion for each topic that has been identified as being “trendy”. The vast majority of posts discussed of Media & Show, while just a relative small portion of comments talked about Health.

The Daily View instead allows to see how each trending topic varies with time, revealing possible interesting trends.

Cross-Topic Analysis

By means of the Cross-Topic Analysis it is possible to understand how different analysis relate one each other. This is performed through the computation of joint frequency tables and conditional probability tables.

For instance, the cross-topic analysis allows to understand how each trending topic is related to the sentiment. The Sentiment X Topic table represents the percentange of occurence (or joint frequency) of each analysed topic with respect to the three level of sentiment (positive, neutral and negative).

It is also possible to estimate the probability of a document or post of being negative, neutral or positive given the fact that it speaks about a certain topic. This is done by means of the conditional probability tables, or using the Polar Plot.

In this analysis the polar plot shows immediately that posts related to Media & Show or Gender Disparity have higher probability of being negative, while the oother are most likely neutral or positive but with a lower probabilty.

However, the cross-topic analysis is not limited to “cross” trending topics with sentiment. It allows to combine together any analysis you are interested in.

Web Opinion Indexes: the new KPI

A Web Opinion Index (WOI) is a synthetic indicator that translates unstructured textual data into structured quantitative information that synthesizes the topic of interest. Essentially, web data are analysed and converted into numeric values by means of the artificial intelligence, and then normalized through well-known statistical techniques.

The result is an index that generates valuable business insights and is both easy to understand and to use. This new KPI, the WOI allows to monitor and compare the evolution of relevant topics.

WOI can be created for every desired sector, brand or products.

OUR ANALYSIS

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