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Artemis Media Forensic

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Media Forensic System which processes social media and news portal data collection automatically based on preset topics and keywords, into in-depth, varied and intuitive visual analysis, making it easier for users to get rich insights easily and quickly.

Key Features

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Quick Setup

In less than 30 minutes, the initial analysis results for data for the past 7 days can be accessed.

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Auto-learning Sentiment

Using Artificial Intelligence to determine the sentiment tone of a conversation.
Manual Correction to improve algorithm accuracy.

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Advanced Social Network

Auto-created conversation clusters,
Tampilan perbincangan antar aktor,
Filter jumlah aktor dalam network.

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Rich Media Metric

important metrics around content distribution, interaction and influencers such as Exposure, Potential Reach, most retweets, most commented, top influencers.

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Emotion Detection

Classify text expressions into 5 types of emotions (neutral, happy, afraid, sad, angry)

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Location Map

number of mentions per region, filtering discussions by regional actors, topic influencers per region

DATA INGESTION ENGINE (CRAWLER ENGINE, SOCIAL-MEDIA AGREGATOR, UNSTRUCTURED COLLECTION ENGINE)

Quick Topic Setup. Fitur penglolaan topik beserta keywords yang relevan / Social Media Public Account yang dibutuhkan untuk dianalisa. Untuk sebuah topik baru, kurang dari 30 menit, hasil analisis awal untuk data 7 hari ke belakang sudah bisa diakses di dashboard. Twitter, Facebook, Instagram, Youtube, Google News, Tiktok Web Service, HOAX detection, Backtrack social media data.

ADVANCED MEDIA FORENSIC ANALYTICAL (MEDIA EXTRACTION ENGINE, ENTITY EXTRACTION, NATURAL LANGUAGE DETECTION)

Sentiment Analysis. Public sentiment analysis uses entity extraction and natural language processing to determine the positive, negative or neutral sentiment tone of a conversation. Analysts can easily correct incorrectly classified sentiments, then increase the accuracy of the analysis results created by the algorithm.

Topic Map. Extracts the main topics from online news portals every hour, displays the most trending topics and most discussed by the media in the last 24 hours so it is useful for predicting new problems that may occur. More detailed topic maps can be created when analysts enter filters related to the sub-topics being analyzed.

Emotion Detection. Detection and identification of types of feelings through text expressions, which are classified into 8 types of emotions such as happiness, trust, fear, surprise, sadness, disgust, anger, and anticipation of anger, disgust, fear, happiness, sadness, and wary.

Location Map. The Location Map feature displays the geographic locations of actors and conversations, giving analysts an understanding of how an issue has spread or resonated.

Site Analysis. The Site Analysis feature makes it easier for analysts to find out which news sites are most active in raising or discussing an issue, reading framing and agendas, making it easier for analysts to develop credible counter-narratives.

Bot Analysis. Bot Analysis shows the proportion of posts and accounts identified as human, cyborg, or bot (robot).

Social Media Metrics. Presents important metrics around content distribution, content interaction and influencers such as Exposure, Potential Reach, most retweets, most liked, most views, most commented, most shared, top influencers.

DATA LAKE ENGINE (ENRICHMENT)

Rich Statistics. Describes the development/trendline of reporting on news portals or posts on social media, along with share of voice based on type and type of media

DATA PRESENTATION ENGINE (VISUAL LINK ANALYSIS)

Social Network and Stakeholder Mapping. Mapping actors, topics, sentiments, locations, media, and groups in various social network analysis and visualization (SNA) with global mode to zoom/detail mode to map stakeholders