Sentilo is an unsupervised, domain-independent
system that performs sentiment analysis by hybridising
natural language processing techniques and semantic
Web technologies. Given a sentence expressing an opinion,
Sentilo recognises its holder, detects the topics and subtopics
that it targets, links them to relevant situations and
events referred by it and evaluates the sentiment expressed on each topic/subtopic. Sentilo relies on a novel
lexical resource, which enables a proper propagation of
sentiment scores from topics to subtopics, and on a formal
model expressing the semantics of opinion sentences.
Sentilo provides its output as a RDF knowledge graph, and whenever possible it resolves holders’ and topics’ identity on Linked Open Data.
Sentiment Analysis also known as opinion mining and Emotional AI
Refers to the use of natural language processing, text analysis, computational linguistics and biometrics to systematically identify, extract, quantify and study affective states and subjective information.
widely used in
Reviews
Survey responses
Online and social media
Health care
Big amount of information is available in textual form in databases or online sources, and for many enterprise functions (marketing, maintenance, finance, etc.) represents a huge opportunity to improve their business knowledge.
Sentiment analysis or opinion mining refers to the application of language processing to identify and extract subjective information in source materials. Generally speaking, sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document.
Supervised Learning Based Approach to Aspect Based Sentiment AnalysisTharindu Kumara
Aspect Based Sentiment Analysis (ABSA) systems receive as input a set of texts (e.g., product reviews) discussing a particular entity (e.g., a new model of a laptop). The systems attempt to
identify the main (e.g., the most frequently discussed) aspects (features) of the entity (e.g., battery, screen) and to estimate the average sentiment of the texts per aspect (e.g., how positive or negative the opinions are on average for each aspect).
Basic introduction of opinion mining and sentiment analysis , challenges faced during opinion mining, Sentiment classification at various levels and application of mining and sentiment analysis
This Project Aimed at doing a comprehensive study of Different Machine Learning Approaches on Sentiment Analysis of Movie Reviews. Support Vector Machines were the one that Performed Most Accurately with Radial Basis Function. Lots of Other kernel functions and Kernel Parameters were tried to find the optimal one. We achieved accuracy up to 83%.
Sentiment Analysis also known as opinion mining and Emotional AI
Refers to the use of natural language processing, text analysis, computational linguistics and biometrics to systematically identify, extract, quantify and study affective states and subjective information.
widely used in
Reviews
Survey responses
Online and social media
Health care
Big amount of information is available in textual form in databases or online sources, and for many enterprise functions (marketing, maintenance, finance, etc.) represents a huge opportunity to improve their business knowledge.
Sentiment analysis or opinion mining refers to the application of language processing to identify and extract subjective information in source materials. Generally speaking, sentiment analysis aims to determine the attitude of a speaker or a writer with respect to some topic or the overall contextual polarity of a document.
Supervised Learning Based Approach to Aspect Based Sentiment AnalysisTharindu Kumara
Aspect Based Sentiment Analysis (ABSA) systems receive as input a set of texts (e.g., product reviews) discussing a particular entity (e.g., a new model of a laptop). The systems attempt to
identify the main (e.g., the most frequently discussed) aspects (features) of the entity (e.g., battery, screen) and to estimate the average sentiment of the texts per aspect (e.g., how positive or negative the opinions are on average for each aspect).
Basic introduction of opinion mining and sentiment analysis , challenges faced during opinion mining, Sentiment classification at various levels and application of mining and sentiment analysis
This Project Aimed at doing a comprehensive study of Different Machine Learning Approaches on Sentiment Analysis of Movie Reviews. Support Vector Machines were the one that Performed Most Accurately with Radial Basis Function. Lots of Other kernel functions and Kernel Parameters were tried to find the optimal one. We achieved accuracy up to 83%.
Sentiment Analysis Using Hybrid Structure of Machine Learning AlgorithmsSangeeth Nagarajan
Sentiment Analysis is the process used to determine the attitude/ opinion/ emotion expressed by a person about a particular topic. The presentation dealt with general approach and different machine learning based classification alogorithms. The slides is based on the work "Sentiment analysis using Neuro-Fuzzy and Hidden Markov models of text" by Rustamov S , Mustafayev E and Clements M A.
Review of Natural Language Processing tasks and examples of why it is so hard. Then he describes in detail text categorization and particularly sentiment analysis. A few common approaches for predicting sentiment are discussed, going even further, explaining statistical machine learning algorithms.
Iulia Pasov, Sixt. Trends in sentiment analysis. The entire history from rule...IT Arena
Iulia Pasov is a senior Data Scientist working for Sixt SE, as well as a PhD student in Artificial Intelligence and Psychology and a WiDS Ambassador. As a Data Scientist, Iulia focuses on building AI-based services meant to optimize car rental processes, as well as pipelines for automatic training and deploying of machine learning models. For her studies, she searches ways to improve learning in online knowledge building communities with the use of artificial intelligence.
Speech Overview:
Sentiment analysis is one of the most known sub-domains of Natural Language Processing (NLP), especially used in the classification of feedback messages. This talk will condense over 15 years of research on different approaches in sentiment analysis, as they evolved during time. The audience will be guided through the advantages and disadvantages of each method, in order to understand how to approach the topic given their needs.
Sentiment analysis is essential operation to understand the polarity of particular text, blog etc. This presentation has introduction to SA and the approaches in which they can be designed.
Sentiment analysis - Our approach and use casesKarol Chlasta
I. Introduction to Sentiment Analysis and its applications.
II. How to approach Sentiment Analysis?
III. 2015 Elections in Poland on Twitter.com & Onet.pl.
Lexicon-Based Sentiment Analysis at GHC 2014Bo Hyun Kim
Attended Grace Hopper Celebration to present the work in Data Science Track. The presentation is on using HP Vertica Pulse and enhancing the accuracy using the right pre-processing methods and training for accuracy using the naive bayes theorem.
Aspect Level Sentiment Analysis for Arabic LanguageMido Razaz
This is the presentation I used in my proposal seminar for master degree in ISSR.
the thesis about Aspect Level Sentiment Classification for Arabic Language.
Any further info. please contact me at (razaz_2006@hotmail.com)
Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets.
We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.
This presentation consist of detail description regarding how social media sentiments analysis is performed , what is its scope and benefits in real life scenario.
One fundamental problem in sentiment analysis is categorization of sentiment polarity. Given a piece of written text, the problem is to categorize the text into one specific sentiment polarity, positive or negative (or neutral). Based on the scope of the text, there are three distinctions of sentiment polarity categorization, namely the document level, the sentence level, and the entity and aspect level. Consider a review “I like multimedia features but the battery life sucks.†This sentence has a mixed emotion. The emotion regarding multimedia is positive whereas that regarding battery life is negative. Hence, it is required to extract only those opinions relevant to a particular feature (like battery life or multimedia) and classify them, instead of taking the complete sentence and the overall sentiment. In this paper, we present a novel approach to identify pattern specific expressions of opinion in text.
Sentiment Analysis Using Hybrid Structure of Machine Learning AlgorithmsSangeeth Nagarajan
Sentiment Analysis is the process used to determine the attitude/ opinion/ emotion expressed by a person about a particular topic. The presentation dealt with general approach and different machine learning based classification alogorithms. The slides is based on the work "Sentiment analysis using Neuro-Fuzzy and Hidden Markov models of text" by Rustamov S , Mustafayev E and Clements M A.
Review of Natural Language Processing tasks and examples of why it is so hard. Then he describes in detail text categorization and particularly sentiment analysis. A few common approaches for predicting sentiment are discussed, going even further, explaining statistical machine learning algorithms.
Iulia Pasov, Sixt. Trends in sentiment analysis. The entire history from rule...IT Arena
Iulia Pasov is a senior Data Scientist working for Sixt SE, as well as a PhD student in Artificial Intelligence and Psychology and a WiDS Ambassador. As a Data Scientist, Iulia focuses on building AI-based services meant to optimize car rental processes, as well as pipelines for automatic training and deploying of machine learning models. For her studies, she searches ways to improve learning in online knowledge building communities with the use of artificial intelligence.
Speech Overview:
Sentiment analysis is one of the most known sub-domains of Natural Language Processing (NLP), especially used in the classification of feedback messages. This talk will condense over 15 years of research on different approaches in sentiment analysis, as they evolved during time. The audience will be guided through the advantages and disadvantages of each method, in order to understand how to approach the topic given their needs.
Sentiment analysis is essential operation to understand the polarity of particular text, blog etc. This presentation has introduction to SA and the approaches in which they can be designed.
Sentiment analysis - Our approach and use casesKarol Chlasta
I. Introduction to Sentiment Analysis and its applications.
II. How to approach Sentiment Analysis?
III. 2015 Elections in Poland on Twitter.com & Onet.pl.
Lexicon-Based Sentiment Analysis at GHC 2014Bo Hyun Kim
Attended Grace Hopper Celebration to present the work in Data Science Track. The presentation is on using HP Vertica Pulse and enhancing the accuracy using the right pre-processing methods and training for accuracy using the naive bayes theorem.
Aspect Level Sentiment Analysis for Arabic LanguageMido Razaz
This is the presentation I used in my proposal seminar for master degree in ISSR.
the thesis about Aspect Level Sentiment Classification for Arabic Language.
Any further info. please contact me at (razaz_2006@hotmail.com)
Most existing approaches to Twitter sentiment analysis assume that sentiment is explicitly expressed through affective words. Nevertheless, sentiment is often implicitly expressed via latent semantic relations, patterns and dependencies among words in tweets. In this paper, we propose a novel approach that automatically captures patterns of words of similar contextual semantics and sentiment in tweets. Unlike previous work on sentiment pattern extraction, our proposed approach does not rely on external and fixed sets of syntactical templates/patterns, nor requires deep analyses of the syntactic structure of sentences in tweets.
We evaluate our approach with tweet- and entity-level sentiment analysis tasks by using the extracted semantic patterns as classification features in both tasks. We use 9 Twitter datasets in our evaluation and compare the performance of our patterns against 6 state-of-the-art baselines. Results show that our patterns consistently outperform all other baselines on all datasets by 2.19% at the tweet-level and 7.5% at the entity-level in average F-measure.
This presentation consist of detail description regarding how social media sentiments analysis is performed , what is its scope and benefits in real life scenario.
One fundamental problem in sentiment analysis is categorization of sentiment polarity. Given a piece of written text, the problem is to categorize the text into one specific sentiment polarity, positive or negative (or neutral). Based on the scope of the text, there are three distinctions of sentiment polarity categorization, namely the document level, the sentence level, and the entity and aspect level. Consider a review “I like multimedia features but the battery life sucks.†This sentence has a mixed emotion. The emotion regarding multimedia is positive whereas that regarding battery life is negative. Hence, it is required to extract only those opinions relevant to a particular feature (like battery life or multimedia) and classify them, instead of taking the complete sentence and the overall sentiment. In this paper, we present a novel approach to identify pattern specific expressions of opinion in text.
Approach to evaluate a user interface based on "the function it communicates", a personal interpretation derived from reflection of the meaning of GUI components and interaction for the user and the user experience.
Presentation at NordiCHI 2014
8th Nordic Conference on Human-Computer Interaction
Helsinki, Finland
October 27, 2014
ACM Press
http://dx.doi.org/10.1145/2639189.2641209
Abstract:
This paper introduces an approach for evaluating user interfaces built on visual rhetoric and the rhetorical notion of function. A personal informatics mobile application has been selected to exemplify the application of this approach. Through the results of this example evaluation, this paper discusses the consequence of applying a rhetorical evaluation to a user interface. In this discussion, it is observed that inspecting the function performed by interface components takes into account experiences, communication, and meaning. In addition, it fosters reflection and criticism.
Sentence level sentiment polarity calculation for customer reviews by conside...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
An Improved sentiment classification for objective word.IJSRD
Sentiment classification is an ongoing field and interesting area of research because of its application in various fields. Customer sentiments play a very important role in daily life. Currently, Sentiment classification focused on subjective statements and ignores objective statements which also carry sentiment. During the sentiment classification, problem is faced due to the ambiguous sense (meaning) of words and negation words. In word sense disambiguation method semantic scores calculated from SentiWordNet of WordNet glosses terms. The correct sense of the word is extracted and determined similarity in WordNet glosses terms. SentiWordNet extract first sense of word which used in general sense. This work aims at improving the sentiment classification by modifying the sentiment values returned by SentiWordNet and compare classification accuracy of support vector machine and naïve bays.
Qualitative methods in Psychology ResearchDr. Chinchu C
An introduction to Qualitative Methods in Psychology. Intended mostly for UG/PG students. Conveys the essentials of Ontology and Epistemology and moves on to the popular methods in Qualitative Psychological Research
A General Architecture for an Emotion-aware Content-based Recommender SystemLucio Narducci
A General Architecture for an Emotion-aware Content-based Recommender System
Fedelucio Narducci, Marco De Gemmis, Pasquale Lops
3rd Empire Workshop
RecSys 2015, Vienna, Austria, 16-20 September 2015
Building the ArCo knowledge graph: process, experience and struggle with exis...Valentina Presutti
Brief description of the ArCo project with remarks on main issues about tool support for the ontology engineering process and some ongoing effort in my Lab to address them.
ArCo: the Knowledge Graph of Italian Cultural HeritageValentina Presutti
ArCo is a very ambitious ontology project. Starting from the official central catalogue of Italian Cultural Heritage (maintained by the Ministry) as its main source, its goal is to release an open knowledge graph encoding knowledge about the entities described in catalogue records. This means going beyond the mere representation of their metadata. Although there's still a long way to go, ArCo reached its first 'stable' version (https://w3id.org/arco). The experience in developing this project has taught us important lessons both in knowledge engineering in general, and on its application to Cultural Heritage. In this talk I will tell ArCo's story and lessons learned focusing on methodological, social and ontological perspectives.
Where are all the Semantic Web agents? There are billions of "machine readable" open facts on the Semantic Web, i.e. Linked Open Data (LOD), isn't that enough? It looks like it's not. We're still far from seeing Lucy's and Pete's agents brilliantly solving their tasks with the help of other Semantic Web agents they can trust (Tim Berners Lee et al., The Semantic Web, Scientific American (2001) ). Despite its technological impact on many applications and areas, the Semantic Web promised to cause a breakthrough that we didn't yet experience. One issue is that LOD ontologies are not as linked as they should be. Another issue is that formalising only semi-structured Web pages or databases is not enough for making them able to operate. They also need to reason with commonsense knowledge, the encoding of which is a long-standing challenge in Artificial Intelligence. A third consideration is that most existing commonsense knowledge bases lack formal semantics and situational constraints. In this talk I will advocate the role of the Semantic Web as a provider of a knowledge graph of commonsense to Artificial Intelligence, and discuss ways and obstacles towards the achievement of this goal.
A machine reader is a tool able to transform natural language text to formal structured knowledge so as the latter can be interpreted by machines, according to a shared semantics. FRED is a machine reader for the semantic web: its output is
a RDF/OWL graph, whose design is based on frame semantics. Nevertheless, FRED’s graph are domain and task independent
making the tool suitable to be used as a semantic middleware for domain- or task- specific applications. To serve this purpose,
it is available both as REST service and as Python library. This presentation gives an overview of the method and principles behind FRED’s implementation.
Knowledge Extraction and Linked Data: Playing with FramesValentina Presutti
To understand somebody who speaks to us or a text we are
reading, we identify the main entities, and how they relate to each other within relation schemas called frames. This means that we first recognise the occurrence of some frames and then we perform some contextualised reasoning over it, where the context is given by the recognised frames. Three main ingredients may enable machines to perform this process: knowledge extraction, knowledge representation and automated reasoning.
The Semantic Web and Linked Data paradigms provide a knowledge representation model enabling sophisticated automated reasoning. Nevertheless, the modelling trend in existing Linked Data ontologies is far from supporting a frame-based reasoning approach. In this talk, I will describe projects that support frame-driven knowledge extraction and representation, both from a design and an empirical perspective.
This tutorial tries to answer the following questions:
What is the best practice for ontology reuse?
Is it fine to use external ontology entities to model my local entities?
Should I import the ontologies that I reuse?
What if I only need a part of an ontology?
What if an external ontology that I reused, changes?
Fueling the future with Semantic Web patterns - Keynote at WOP2014@ISWCValentina Presutti
I will claim that Semantic Web Patterns can drive the next technological breakthrough: they can be key for providing intelligent applications with sophisticated ways of interpreting data. I will picture scenarios of a possible not so far future in order to support my claim. I will argue that current Semantic Web Patterns are not sufficient for addressing the envisioned requirements, and I will suggest a research direction for fixing the problem, which includes the hybridisation of existing computer science pattern-based approaches, and human computing.
Nucleophilic Addition of carbonyl compounds.pptxSSR02
Nucleophilic addition is the most important reaction of carbonyls. Not just aldehydes and ketones, but also carboxylic acid derivatives in general.
Carbonyls undergo addition reactions with a large range of nucleophiles.
Comparing the relative basicity of the nucleophile and the product is extremely helpful in determining how reversible the addition reaction is. Reactions with Grignards and hydrides are irreversible. Reactions with weak bases like halides and carboxylates generally don’t happen.
Electronic effects (inductive effects, electron donation) have a large impact on reactivity.
Large groups adjacent to the carbonyl will slow the rate of reaction.
Neutral nucleophiles can also add to carbonyls, although their additions are generally slower and more reversible. Acid catalysis is sometimes employed to increase the rate of addition.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
2. Aldo Gangemi, Valentina Presutti, Diego Reforgiato Recupero:
Frame-Based Detection of Opinion Holders and Topics: A Model and a Tool.
IEEE Comp. Int. Mag. 9(1): 20-30 (2014)
Diego Reforgiato Recupero, Valentina Presutti, Sergio Consoli, Aldo Gangemi,
Andrea Giovanni Nuzzolese:
Sentilo: Frame-Based Sentiment Analysis. Cognitive Computation 7(2): 211-
225 (2015)
3. The talk is about
• Opinion modeling
• Sentiment analysis
• Indirect sentiment analysis
• Frames as sentiment interpretation context
• Sensitivity and factual impact: attributes of thematic
roles as parameter for sentiment computation
• Ontologies, tools, resources
4. What’s an opinion
An intentional statement by somebody (holder) on some fact
(topic) that is expressed with a possible sentiment
5. More formally
The goal of Sentiment Analysis is to detect quintuples
(ej, ajk, soijkl, hi, tl) from unstructured text, where an
opinion is a quintuple [1,2]:
(ej, ajk, soijkl, hi, tl)
where:
ej is a target entity
ajk is an aspect/feature of the entity ej
soijkl is the sentiment value of the opinion from opinion holder hi on aspect ajk of entity
ej at time tl. soijkl is positive, negative or neutral, or a rating
hi is an opinion holder.
tl is the time when the opinion is expressed.
[1] “Sentiment Analysis and Subjectivity”. Bing Liu. Handbook of Natural Language Processing, 2010.
[2] “Sentiment Analysis and Opinion Mining”. Bing Liu. Morgan & Claypool Publishers. May 2012
6. Sentiment analysis
• To extract opinions from text
• To recognise the attitude (positive, negative or
objective) of an opinion holder on a certain topic
• To evaluate the overall tonality of a document
• Document- or sentence-based
7. Semantics into Sentiment Analysis
• Traditional approaches hardly cope with subtle linguistic forms,
combined and concurrent positive/negative opinions, and implicit
judgements
• The literature shows evidence that the inclusion of semantic
features in sentiment analysis algorithms improves their overall
performance, e.g. [3]
• Linked data, ontologies, controlled vocabularies, and lexical
resources help aggregating the conceptual and affective
information associated with natural language opinions
[3] “Semantic Sentiment Analysis of Twitter”, H. Saif, Y. He, and H. Alani, Boston, UA, pp. 508–524, 2012. Springer.
8. Implicit and indirect sentiment analysis
“People hope that The President will be condemned.”
9. Implicit and indirect sentiment analysis
“People hope that The President will be condemned.”
10. Implicit and indirect sentiment analysis
“People hope that The President will be condemned.”
11. Implicit and indirect sentiment analysis
“People hope that The President will be condemned.”
triggering events
opinion holders
main topics
subtopics
indirect impact of sentiment on subtopics
12. http://wit.istc.cnr.it/stlab-tools/sentilo/
Aldo Gangemi, Valentina Presutti, Diego Reforgiato Recupero:
Frame-Based Detection of Opinion Holders and Topics: A Model and a Tool. IEEE Comp. Int. Mag. 9(1): 20-30 (2014)
Diego Reforgiato Recupero, Valentina Presutti, Sergio Consoli, Aldo Gangemi, Andrea Giovanni Nuzzolese:
Sentilo: Frame-Based Sentiment Analysis. Cognitive Computation 7(2): 211-225 (2015)
13. What’s behind Sentilo
• Neo-davidsonian assumption: events and situations
are primary entities for contextualising opinions
• Frames: as reference models for formally
representing opinionated text
• OntoSentilo: an ontology for opinion sentences
• Levinopinion: a revision of Levin’s classification of verbs
for the opinion and sentiment analysis task
• SentiloNet: a resource of ~1000 annotated verbal
frames with role sensitivity and factual impact
15. Frame-based representation of text (FRED)
“People hope that The President will be condemned by the judge.”
http://wit.istc.cnr.it/stlab-tools/fred [4]
[4] “Semantic Web machine reading with FRED”, A. Gangemi, V. Presutti, D. Reforgiato Recupero,
A. G. Nuzzolese, F. Draicchio, M.Mongiovì, Semantic Web journal, to appear.
19. Levinopinion
Verbs such as accept, agree,
think, say, tell, etc. that
indicate the presence of an
opinion holder who is the
subject of the underlying
verb.
Verbs such as contest,
disagree, dismiss, oppose,
etc. that indicate the
presence of an opinion
holder, who is the subject
of the underlying
verb; subjects of such
verbs have an opinion
which is in contrast with
whatever is expressed in
the opinionated context.
Verbs such as dislike, hate,
etc. These verbs indicate
the presence of an opinion
holder expressing a
negative sentiment on
some topic(s). This
class of verbs is equivalent
to the previous one when
a negation occurs.
Verbs such as love, like,
honor, support, etc. These
verbs indicate the presence
of an opinion holder
expressing a positive
sentiment on some topic(s).
“The commission agreed on a proposal to limit imports”
“I support the cause”
“A majority of the electorate opposed EC membership.”
“He hates flying”
21. Topic detection
• Two equivalence classes of VerbNet roles
• AGNT: all agentive roles
• PTNT: all passive roles
• Main topics: all PTNT of a trigger event or (almost)
all entities having only ongoing arcs
• What about subtopics?
23. Subtopic detection: issues
• How to distinguish subtopics that are indirectly
affected by an opinion from those that are not?
• How to evaluate the polarity of the sentiment
indirectly expressed on them?
24. Specialising dependsOn
• sentilo:participatesIn: all potential subtopics. Entities involved in
dul:Situation or playing a role in a dul:Event, when they are MainTopic
• sentilo:playsSensitiveRole: connects a main topic to a subtopic, meaning that
the latter may be indirectly affected by an opinion expressed on the former
• sentilo:isPositivelyAffectedBy: a sensitive subtopic that will inherit the same
sentiment of its main topic
• sentilo:isNegativelyAffectedBy: a sensitive subtopic that will inherit the
opposite sentiment of its main topic
26. SentiloNet
• Role sensitivity:
• A role is sensitive with respect to an event if it is
indirectly affected by an opinion (directly)
expressed on the event.
• Sensitivity is an attribute of semantic roles. It can be
true or false.
27. SentiloNet
• Factual Impact:
• Indicates that an event has an expected impact on
the player of a specific role.
• It is an attribute of sensitive roles: It takes either a
positive or a negative value.
28. SentiloNet examples
Verb S-AGNT S-PTNT F-AGNT FPTNT
abandon F T neg
achieve T T pos pos
condemn F T neg
http://www.stlab.istc.cnr.it/documents/sentilo/sentilonet.zip
29. Potential subtopics,
sensitive roles and factual impact
1100 annotated verbs with values for sensitivity and
factual impact for all roles in AGNT and PTNT roles
“People hope that The President will be condemned by the judge.”
30. Sentiment propagation
topic
Combined score
from Sentic.net and
SentiWordNet
t dul:hasQuality qi
t rdf:type typei(t)
t boxing:hasTruthValue fred:False
t boxing:hasTruthValue fred:True
opinion trigger verb
possible context of t
a situation or an event
in which t participates
modality of t
31. Combined individual sentiment score
SentiWordNet: http://sentiwordnet.isti.cnr.it
SenticNet: http://sentic.net
• dul:hasQuality, dul:Event (sentilo:hasOpinionTrigger)
• SenticNet provides only one value per word (if any), SentiWordNet
provides one value per sense
• Disambiguating is time-consuming
• We combine the SentiWordNet score for the most frequent senses with
SenticNet score using a simple heuristics
32. Combined individual sentiment score
• Sort all most frequently used senses for a word w in
decreasing order of frequency
• Keep in the list of most frequent senses for w only
those senses that have a frequency higher than 10%
of the previous one
• Retrieve all SentiWordNet scores for selected
senses and compute their average sWN
• Retrieve the SenticNet score sNet for w
• Compute the average between sWN and sNet
37. Correlation tests
• Overall sentence sentiment polarity
• Open rating user reviews (TripAdvisor)
• Randomly selected 50 positive and 50 negative
reviews and computed correlation
38. Conclusion and Open issues
We discussed
• Importance of cognitive approach to sentiment analysis: indirect/implicit sentiment
• Frame representations are powerful for interpreting opinion contexts
• Sentilo, Levinopinion, SentiloNet
We are looking forward
• To investigate how this approach may work for aspect-based sentiment analysis
• To investigate how this approach may work for detecting irony and sarcasm
• To exploit additional resources, e.g. Framester, which includes DepecheMood and
relations among frames
39. References
In academic publication, as reference to Sentilo please cite:
Aldo Gangemi, Valentina Presutti, Diego Reforgiato Recupero: Frame-
Based Detection of Opinion Holders and Topics: A Model and a Tool. IEEE
Comp. Int. Mag. 9(1): 20-30 (2014)
Diego Reforgiato Recupero, Valentina Presutti, Sergio Consoli, Aldo
Gangemi, Andrea Giovanni Nuzzolese: Sentilo: Frame-Based Sentiment
Analysis. Cognitive Computation 7(2): 211-225 (2015)
As reference to FRED please cite:
“Semantic Web machine reading with FRED”, A. Gangemi, V. Presutti, D.
Reforgiato Recupero, A. G. Nuzzolese, F. Draicchio, M.Mongiovì, Semantic
Web journal, to appear.
40. References
Other relevant references related to the FRED project:
Aldo Gangemi, Andrea G. Nuzzolese, Valentina Presutti, and Diego Reforgiato Recupero. Adjective semantics in open
knowledge extraction. In FOIS 2016, pp.167-180. http://ebooks.iospress.nl/volumearticle/44244. DOI: 10.3233/978-1-61499-
660-6-167
Aldo Gangemi: A Comparison of Knowledge Extraction Tools for the Semantic Web. ESWC 2013: 351-366.
https://link.springer.com/chapter/10.1007/978-3-642-38288-8_24. DOI: 10.1007/978-3-642-38288-8_24
Valentina Presutti, Francesco Draicchio, and Aldo Gangemi. Knowledge extraction based on discourse representation theory
and linguistic frames. EKAW 2012. https://link.springer.com/chapter/10.1007%2F978-3-642-33876-2_12.DOI:10.1007/978-3-
642-33876-2_12 .
Valentina Presutti, Andrea Giovanni Nuzzolese, Sergio Consoli, Aldo Gangemi, Diego Reforgiato Recupero: From hyperlinks
to Semantic Web properties using Open Knowledge Extraction. Semantic Web 7(4): 351-378 (2016).
http://content.iospress.com/articles/semantic-web/sw221. DOI: 10.3233/SW-160221
Aldo Gangemi, Andrea Giovanni Nuzzolese, Valentina Presutti, Francesco Draicchio, Alberto Musetti, Paolo Ciancarini:
Automatic Typing of DBpedia Entities. International Semantic Web Conference (1) 2012: 65-81.
https://link.springer.com/chapter/10.1007/978-3-642-35176-1_5. DOI: 10.1007/978-3-642-35176-1_5
Misael Mongiovì, Diego Reforgiato Recupero, Aldo Gangemi, Valentina Presutti, Sergio Consoli: Merging open knowledge
extracted from text with MERGILO. Knowl.-Based Syst. 108: 155-167 (2016).
http://www.sciencedirect.com/science/article/pii/S0950705116301034
41. References
Other relevant references
“Sentiment Analysis and Subjectivity”. Bing Liu. Handbook of Natural
Language Processing, 2010.
“Sentiment Analysis and Opinion Mining”. Bing Liu. Morgan & Claypool
Publishers. May 2012
“Semantic Sentiment Analysis of Twitter”, H. Saif, Y. He, and H. Alani,
Boston, UA, pp. 508–524, 2012. Springer.