News Summary Extraction
The News Summary Extraction feature is a cutting-edge tool for the news industry. It utilizes advanced technology to automatically generate summaries of journalist texts, providing a quick and easy way for readers to stay informed without having to read every article in a cluster. This feature offers a concise and comprehensive overview of news.
Ngram Example
“Summaries” offer state-of-the-art technology in journalistic text summarization. The summarization engine is currently applied to news clusters (i.e. the groups of news texts that refer to the same event). It processes all the texts in the cluster using natural language processing techniques and graph algorithms and extracts a small but comprehensive summary that contains the most essential topics, the highlights of the cluster. These most important aspects of a news cluster are presented at a glance to the user that wants to have a quick view of what the cluster talks about.
When new articles are added to the cluster, the ‘Summaries’ are updated to correspond to the current status of information.
Summary Example
Sentiment Analysis
Sentiment analysis is a technique used to understand the emotions and opinions expressed in texts about specific entities, such as companies, individuals, or products. Our algorithms can identify the entities mentioned in a text, recognize different ways they are written (e.g. full name or initials), and detect words that express positive or negative opinions. Additionally, we use syntactic analysis to determine the overall emotional tone of the text and aggregate opinions to determine the overall emotional impact of an entity.
NER & Aspect Mining
Name Entity Recognition
Name Entity Recognition provides semantic knowledge over the collected data and helps you quickly understand the subject of the content. It is one of the most common starting points for using natural language processing techniques to enrich any content. With this service its easier to provide more accurate sentiment analysis and give more clear results and search capabilities to our services.
Aspect Mining
Aspect mining is a technique that aims to identify and classify recurring topics or themes in different types of content, such as news articles and social media posts. It can be used to gain insights into common aspects of customer reviews and classify them based on certain features. This can be useful for marketing and sales purposes, and is a subfield of opinion mining that focuses on determining the specific aspects discussed in a given text.
Content Aggregation & Clustering
Content Aggregation
Content aggregation and management is a fully automated process that involves collecting, analyzing and organizing information from various sources on the web, such as news articles, blog posts, multimedia content and social media comments. The process uses web crawling, which is the practice of automatically visiting websites and extracting information from them, as well as contextual analysis and text clustering to group similar content together.
Our system is designed specifically for big data crawling, and our search index is one of the largest in Europe. The user interface is designed to be easy to use, with intuitive filters and reporting capabilities.
Content Clustering
Content clustering is ideally suited to build on its distinctive technical and research and development capabilities to provide enhanced intelligence insights. It works on top of a large text repository and generates groups of similar texts (clusters) that represent how an event is reported by various digital media sources.
Palowise examines thousands of texts from various sources and types, and ultimately organizes them into related clusters using an innovative statistical framework based on widely used techniques in science and engineering. Clustering has been shown to be an effective technique for information retrieval by uncovering valuable information kernels and distributions in the underlying data. As a result, it can provide information retrieval systems with the ability to improve the user’s experience while browsing and quickly identifying the desired information. In our case, we combine text and data from various sources and types, and based on weighted characteristic features, we process these in a way that ultimately allows the user to read a comprehensive coverage of a topic with different levels of hierarchy articles. The more input data we have, the more closely they are related in the analysis and they fully cover the topic.
ICT4Growth project
ICT4Growth is a research project that Palowise undertook to develop algorithmic software that fuels its innovative platform. More specifically the R&D areas which Palowise Services targeted and implemented were the following:
Automatic Resource Discovery
We have developed an efficient process that identifies and discovers new sources of information related to entities such as companies and organizations. This process, which is at the top level, generates data that can then be analyzed using natural language processing techniques by other processes.
Named Entity Recognition
The process of disambiguation is used to distinguish actual entities, such as a specific company or person, from references in text that are not related to a specific entity. This process enhances the meaning of any text content by identifying and tagging recognized entities, as well as grouping multiple mentions of the same entity under a single name. This helps to provide more meaningful information from text excerpts.
Sentiment Analysis
Sentiment analysis is a technique used to identify and extract the underlying opinion or emotion expressed in a piece of text. It can reveal whether the sentiment expressed is positive, negative or neutral. This technique can be applied to entities extracted by Named Entity Recognition, providing additional information such as the overall reputation of an organization or individual, or the impact of a specific event related to the entity. This helps to understand the real content of a text excerpt more deeply.
Automatic summarization
This process involves grouping together articles that discuss the same topic by first clustering them based on their content. By doing so, it is able to generate a brief but comprehensive summary of a group of articles, providing a quick overview of the text that may be spread across multiple paragraphs. This summary is semantically accurate and offers a quick understanding of the topic.
Aspect Mining
Aspect mining is a technique that enables the identification of different perspectives or themes present in text such as news and social media. It can be applied to the entities identified by named entity recognition to reveal insights about specific entities in the form of different aspects or “views”.
The aforementioned algorithms, in combination with other technologies and a robust infrastructure, form the core framework of Palowise’s services. These services offer a wide range of capabilities, from innovative news reading applications to specialized brand reputation analysis.
Why Palowise?
- #1:Use the industry's top artificial intelligence to handle the heavy work for you and gain insights in minutes.
- #2:Receive an alert if something major occurs near your customer.
- #3:Identify the influencers, material, and messaging required to generate success in real-time.
- #4:Manage cross-channel campaigns with multidisciplinary groups and infinite channels.
- #5:Monitor engagement and sentiment to get valuable insights.
- #6:Monitor trending topics of discussion among users.
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