{"id":10193,"date":"2018-07-04T10:08:59","date_gmt":"2018-07-04T08:08:59","guid":{"rendered":"http:\/\/www.iic.uam.es\/?page_id=10193"},"modified":"2018-09-14T11:47:06","modified_gmt":"2018-09-14T09:47:06","slug":"sentiment-analysis","status":"publish","type":"page","link":"https:\/\/www.iic.uam.es\/en\/artificial-intelligence\/natural-language-processing\/sentiment-analysis\/","title":{"rendered":"Sentiment analysis"},"content":{"rendered":"[vc_row content_placement=”middle” text_align=”center” top_padding=”10″ bottom_padding=”0″][vc_column column_padding=”padding-4″ width=”1\/1″]
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\u202aSentiment analysis<\/span><\/h3>\n[\/vc_column_text][vc_column_text]\n

Lynguo automatically extracts a\u00a0sentiment analysis<\/strong>\u00a0from the opinion, emotion or attitude that is inferred from a given text in order to obtain value from the multiple opinions, criticisms and assessments that users spontaneously make about a brand or competition.<\/p>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row bg_color=”#f7f7f7″ text_align=”center” top_padding=”40″ bottom_padding=”30″][vc_column width=”1\/1″][vc_column_text]\n

How do we analyse s<\/span>entiment<\/span>?<\/span><\/h3>\n[\/vc_column_text][vc_column_text]\n

Sentiment analysis uses the Natural Language Processing<\/strong> to ascertain the opinion of a message on social networks, that is, whether it is positive, negative or neutral. The Lynguo tool performs an analysis at different levels to ensure a greater degree of accuracy than other NLP engines:<\/p>\n[\/vc_column_text][vc_row_inner][vc_column_inner width=”1\/4″]

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Lexical level<\/h3>

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Syntactic level<\/h3>

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Semantic level<\/h3>

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Level of relationship between concepts or context<\/h3>

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Sentiment Analysis<\/strong> will help you to identify opportunities or anticipate and solve possible reputational crises.<\/p>\n[\/vc_column_text]

<\/div>Do you want to perform a Sentiment Analysis?<\/a>[\/vc_column][\/vc_row][vc_row equal_height=”yes” type=”full_width_section” top_padding=”0″ bottom_padding=”0″][vc_column bg_color=”#3591c4″ column_padding=”padding-3″ column_center=”true” text_color=”light” width=”1\/2″]

Lynguo’s customized engine distinguishes sentiment within context and theme.<\/h2>[\/vc_column][vc_column column_padding=”padding-3″ width=”1\/2″][vc_column_text]\n

The Lynguo engine, with customization capability, provides an even higher level of accuracy. While generic engines that perform sentiment analysis offer only tags with the opinion of the comment (positive, negative or neutral), the Lynguo engine is customized according to the context and theme of each sector, providing more value and knowledge.<\/p>\n

Lynguo distinguishes sentiment within context and theme<\/strong>. In this example, the context is the automotive industry, while the themes are power and price. \u2018Power\u2019 is related to a negative sentiment (\u2018low\u2019) and \u2018price\u2019 with a positive sentiment (\u2018very affordable\u2019).<\/p>\n[\/vc_column_text][vc_column_text]\n

The car is low<\/span> power<\/strong> but the price<\/strong> is very affordable<\/span>.<\/em><\/p>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=”full_width_section”][vc_column width=”1\/1″]

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Analysis of emotions<\/span><\/h3>\n[\/vc_column_text][vc_column_text]\n

It automatically detects the emotions of user comments<\/strong> on social networks, from love and joy to sadness and anger.<\/p>\n

This analysis of data is very useful for companies, for example, to know how users respond to brands: detecting anger<\/strong> or satisfaction of customers telephoning Call Centres, how consumers value products, if they are happy or unhappy with their purchase, etc.<\/p>\n[\/vc_column_text][\/vc_column][vc_column width=”1\/2″]

\"An\u00e1lisis<\/a><\/div>[vc_column_text]\n

Example of how a certain brand awakens mostly emotions of love and understanding on the positive side, and fear and uneasiness on the negative side.<\/p>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row content_placement=”top” bg_image=”10228″ bg_repeat=”no-repeat” parallax_bg=”true” text_color=”light” text_align=”center” top_padding=”50″ bottom_padding=”50″][vc_column width=”1\/1″]

Thanks to the team of linguistic experts, the tool maintains its accuracy, improving and maintaining linguistic models.<\/div>[\/vc_column][\/vc_row][vc_row top_padding=”40″ bottom_padding=”20″][vc_column width=”1\/1″][vc_column_text]\n

Analysis by category<\/span><\/h3>\n[\/vc_column_text][vc_column_text]\n

Discover the sentiment of users regarding a specific feature (pricing, technical service, after-sales service, usability, advertising, branding …) for each of the brands monitored.<\/p>\n

Analysis by category<\/strong> provides a very clear vision of how a product, service or brand is positioned, and is very effective in projects of brand reputation or user engagement, helping brands to direct their messages or marketing campaigns in order to improve the image it is transmitting.<\/p>\n[\/vc_column_text]

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In the example we can see that the strength of Brand 1 is its Image. It has no negative opinions and the Image category is very positive. On the other hand, Brand 3 is strong in Product, but it must improve its strategy in the Service and Image categories.<\/p>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row bg_color=”#2b99cc” text_color=”light” text_align=”center” top_padding=”20″ bottom_padding=”15″][vc_column width=”1\/1″][vc_column_text]\n

Lynguo automatically extracts a\u00a0sentiment analysis<\/strong> from the opinion, emotion or attitude.<\/p>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row top_padding=”40″ bottom_padding=”40″][vc_column width=”1\/1″][vc_column_text]\n

Contacta con nosotros<\/span><\/h3>\n[\/vc_column_text]\n
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