In this paper, we present CogNet3, an extension of the CogNet2 knowledge base, which combines the dynamic emotional knowledge of personality groups towards significant events from real word data on Reddit. It aims to structurally model and correlate the subjective emotional knowledge embedded in events. To model the dynamic and complex multi-dimensional emotional information of diferent types of people towards complex events, we construct three frames, namely Semantic Event, Homophilous Group, and Group Emotion, which are respectively used to model hierarchical organizational events with emotional information, user groups with representative diferences in personality attributes, and the multi-dimensional dynamic emotional distribution between user groups and events. To expand the knowledge scale and enhance scalability, we design a LLM information extraction framework with self-verification capabilities for the automated extraction of subjective knowledge information. As a result, in comparison with CogNet2, CogNet3 increases 462,381 new event instance with emotion association, 21,870 diferent homophilous groups and up to 4,556,057 emotion distribution instances.
Modeling human subjective knowledge is critical for advancing applications across domains such as
public opinion analysis [
Although modeling subjective dynamic emotions is of great significance, at the current stage, there is both a lack of an efective structured system for modeling event-centered subjective knowledge and an absence of valid methods for modeling the personality of user groups.
Existing works either treat emotional expressions as simple binary emotional labels or fail to structurally model the original comment text content, which limits their ability to associate and express the complex emotions between users and events. Correspondingly, human emotions toward events are often diverse and complex. In the case of a vicious incident, while most people would experience anger, diferent groups of individuals might additionally feel fear, sadness, or surprise, respectively. Such complex and compound emotional states cannot be adequately summarized by a simple positive or negative label. Furthermore, the structured modeling of these emotions is conducive to accurately associating similar subjective emotional knowledge, which can be applied to prediction and analysis.
Sentiment Modeling: Existing approaches to sentiment analysis predominantly adopt one of two paradigms: either modeling emotional expressions as binary positive/negative labels or neglecting the structural complexity of raw textual content [14, 15, 16, 17]. Both strategies inadequately capture the intricate emotional relationships between users and events. Human emotional responses to events exhibit a multifaceted nature that transcends simple categorical labels. For instance, while a vicious incident may universally evoke anger, it might simultaneously elicit fear, sorrow, or distraction across diverse user groups. Such compound emotional states resist reduction to binary classifications and necessitate richer representational frameworks. Structured modeling of these emotions facilitates the precise association of similar subjective emotional knowledge, which can be leveraged for prediction and analysis.
User Modeling: Current methods abstract users into static, objective user profiles including attributes like gender, age, and occupation [18, 19, 20]. However, these attributes exert indirect and implicit influences on subjective emotional responses to events. For example, two individuals with identical demographic profiles may exhibit divergent emotional reactions to the same event due to diferences in personality traits. Conversely, users sharing similar personality characteristics tend to demonstrate consistent emotional patterns and viewpoints. However, modeling personality traits, which are critical latent factors in subjective emotion expression, presents significant challenges. First, overly granular personality taxonomies risk amplifying intra-group variability while diminishing generalizability, as ifne-grained traits may become overly instance-specific. First, the schema of personality struggles to balance accuracy and practicality. The more sophisticated a personality modeling system is, the smaller the emotional diferences within groups; however, the subjective knowledge derived from excessive detail lacks representativeness due to its bias toward a limited number of individual instances. On the other hand, users’ personality traits are implicit, dark knowledge, which is dificult to acquire and analyze eficiently.
To address these challenges, this paper presents CogNet3 with subjective emotion knowledge, an extension of a frame-based, multi-source knowledge fusion graph, CogNet2 [21], that integrates diverse knowledge types. CogNet3 focuses on modeling public emotional responses to events, building upon CogNet2’s core frame structure for enhanced knowledge representation. Specifically, we introduce three novel frames customized for subject knowledge structuralization: Emotional Event Frame, Homogeneous Group Frame, and Group Emotion Frame. The Emotional Event Frame captures structured information about events (e.g., attributes, participants, and context) and their inherent emotional triggers. The Homogeneous Group Frame models user groups with shared personality profiles, derived from analyzing behavioral and linguistic patterns of real users. The Group Emotion Frame links these groups to their emotional distributions toward specific events, quantifying the intensity and diversity of responses within each group. To enable the scalable construction of CogNet3, we propose an automated, largescale emotional knowledge extraction framework with built-in consistency verification, ensuring the reliability of extracted knowledge.
In summary, CogNet3 has three improvements as follows. (1) Structure. It increases three specific frames for modeling the dynamic and complex subjective emotions of groups with diferent personalities towards events. (2) Expansibility. It is automatically constructed and verified by LLMs based on social media data. (3) Scale. It consolidates a larger scale of subjective knowledge instances. Currently, CogNet3 increases 462,381 new event instance, 21,870 new homophilous group and the scale of group emotion is up to 4,556,057 in total. The data, code and online demo is available at http://cognet.top/v3/.
The frame design architecture of tsuCmhorooenegdasN1elai.letmiytn3UegafstoHietsrrroissiblmuluwubtosjeiettpsrchhataitriivedeledoeakunisnntssioicgtGwhanrleleeodpFdueigtgproe-- PTCrraeumsmipdpae'isngt2nia0l24 CPhairledn..E.tvEevnetnt aTCnroudnmtPrpaonlWaGmirseaheCtnolaanEnvaedlnt E..m.otioATncsimtiveists D2o0n2a4l-d1T2r-u2m4p fervaemnteinesntatintyce.s aArellanddewitiloynaaldlydeadt- SCAtogarnnesceiesatbenlecnyess SRLtiaogbwhlet Group AAnntgiecripation 1N900o0%n%e 1L00o%%w Me00d%%ium H00i%%gh tributed to the Semantic Event Conscientiousness Medium Disgust 0% 0% 20% 80% frame. The emotional distribution ... ... ... ... ... ... ... from each personality group to emotional events is modeled us- Frame: Sentiment Event Frame: Homophilous Group Frame Element (FE) ing Group Emotion. We utilize Frame: Person Frame: Group Emotion ... FE (One-to-Many) Reddit 1 data from the past two years as the source for informa- Figure 1: Illustration of the Data Model. tion extraction. We screen popular submissions and active users from six news-related subreddits such as r/news and r/politics, and associate their comments with corresponding emotional distribution information. Meanwhile, submissions and their corresponding comments from the r/askreddit are retained to supplement the modeling of user personalities.
In terms of emotional event modeling, we define a new frame, Semantic Event, which includes frame elements such as emotion, time, parent event, and child events. By leveraging the occurrence time of events and their hierarchical relationships, the public emotions of each sub-event can be temporally linked, thereby modeling the dynamic changes in public emotions corresponding to significant events. We select submissions with more than 5 comments and use large language models (LLMs) [22, 23, 24] for event type classification to associate them with the existing event frame system of CogNet2. Furthermore, LLMs are employed to summarize superordinate event labels. To merge all sub-events corresponding to a significant event, we first use vector semantic representations to perform K-Means clustering on the superordinate event labels summarized by LLMs, selecting the label of the cluster center as the label for the significant event of the entire cluster. Then, the LLM is used to judge each sub-event against the parent label, removing incorrect hierarchical relationships.
Our modeling system for user personalities includes ideology, expression habits, and the Big Five personality traits [25, 26], which can comprehensively reflect individual personality characteristics. We associate real users’ comment records within a specified time range. For users with more than 5 historical comments in all target subreddits, we randomly select up to 100 comments in chronological order and use LLMs to conduct in-depth rational analysis of these three aspects of personality traits for labeling. Attributes involving fixed labels include stance tendency (far left, left, centrist, right, far right) and stance firmness (stable, depends). Attributes involving low, medium, and high three degrees of labels include the aggressiveness and logicality of expression, as well as the Big Five personality traits: openness, conscientiousness, extraversion, agreeableness, and neuroticism.
To model group emotions toward events, we separately model the emotional responses of crowds to events as a Group Emotion frame, whose frame elements include events, user groups, and emotional distribution. We use LLMs to conduct in-depth rational analysis of comment information from users with labeled personality profiles among all annotated emotional events, obtaining complex and multi-dimensional emotional distributions. For emotion modeling, we refer to Plutchik’s Wheel of Emotions [27], labeling each of the 8 dimensions (Anger, Fear, Sadness, Joy, Disgust, Surprise, Trust, Anticipation) with four degree labels: none, low, medium, and high. Based on the emotional information representation of these eight dimensions, 8 types of compound emotions can be further derived automatically. We associate the emotional representations analyzed from individual users’ comments with their corresponding user personality groups. Since multiple users belonging to the same user personality group may comment on an event, the emotional information of all users in the same personality group is aggregated to obtain the corresponding emotional distribution. The final representation of group emotions is in the form of a continuous distribution of 4 degrees for each of the 8 dimensions, with the proportion of each label expressed as a percentage.
To enhance the accuracy and rationality of the extraction results, we adopt strategies of rejection sampling and consistency verification. If the output of the LLM does not meet the schema and format requirements, it is automatically retried until a valid output is obtained. Among n valid outputs, a result is considered the final output only if it receives the same annotation at least twice. In the final construction of the knowledge graph, manual sampling and calibration are performed on the subjective knowledge extracted by the LLM.
We provide an online platform for querying and visualizing CogNet3 (http://cognet.top/v3/). The website includes detailed user instructions and case introductions, covering functions such as top-down querying of event hierarchies, querying and associating user emotions with events, and analyzing the changes in users’ emotions over associated timelines. All data of CogNet3 and its construction codes are available for download under the CC-BY-SA 4.0 license.
This work is supported by the National Natural Science Foundation of China (No.U24A20335, No. 62176257, No.62576340). This work is sponsored by Beijing Nova Program (No.20250484750) and supported by Beijing Natural Science Foundation(L243006). This work is also supported by the Youth Innovation Promotion Association CAS.
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