Live broadcast data processing method and system

Abstract

The invention discloses a live broadcast data processing method and system, and relates to the technical field of data processing. The live broadcast data processing method comprises the following steps: acquiring user behavior data of a current time period in real time, respectively performing data analysis to obtain an interaction index and a preference index of the current time period, acquiring text information of the current time period, and performing language analysis processing to obtain a comment emotion index of the current time period in a live broadcast process; according to the method and the device, the corresponding live broadcast measures are taken for the next time period based on the live broadcast effect feedback index of the current time period in the live broadcast process, so that the live broadcast content and the interaction strategy are optimized in real time; and therefore, the live broadcast content and the strategy can be adjusted more flexibly and accurately, and the viscosity and the activeness of audiences can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a live broadcast data processing method and system. Background Art

[0002] With the continuous development of Internet technology, the live broadcast industry has become one of the important ways of digital entertainment and information dissemination. From the initial social live broadcast platforms to today's diversified applications such as video conferencing, online education, and e-commerce live broadcasts, live broadcasts have profoundly changed people's lifestyles and working methods. Especially in recent years, with the continuous progress of live broadcast technology, the interactivity and real-time nature have been enhanced, and the communication between live broadcast platforms and users has become closer.

[0003] In the prior art, traditional live broadcast data processing methods usually rely on simple data collection and storage, and it is difficult to deeply explore the potential value behind the data. Although many live broadcast platforms can collect a large amount of user behavior and interaction data, there is still a certain lag in data processing and analysis, and it is difficult to obtain accurate user preferences and needs in real time.

[0004] The limitations of the prior art at least include the following problems. In the prior art, interaction data and preference data are separated and lack effective combination, making it difficult to comprehensively evaluate user interests. Therefore, it is difficult to adjust the content in a timely manner according to real-time audience behavior and interaction during the live broadcast. Moreover, the prior art is difficult to capture the dynamic changes of the audience's emotions in real time and ignores the emotional feedback in the interaction data, resulting in a lack of comprehensive effect evaluation and dynamic adjustment. Furthermore, it is difficult to conduct accurate effect evaluation and content optimization, and it is easy to lead to difficulties in making corresponding live broadcast strategy adjustments in real time. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a live broadcast data processing method and system, which solves the problems in the prior art that interaction data and preference data are separated, and it is difficult to capture the emotional changes of the audience in real time, resulting in a lack of comprehensive evaluation and dynamic adjustment, and further difficult to accurately optimize content and strategies.

[0006] To achieve the above object, the present invention is realized through the following technical solutions: A live broadcast data processing method includes the following steps: obtaining the user behavior data of the current period during the live broadcast in real time; respectively performing data analysis on the user behavior data of the current period during the live broadcast to obtain the interaction index and preference index of the current period during the live broadcast; simultaneously obtaining the text information of the current period during the live broadcast, and performing language analysis and processing to obtain the comment emotion index of the current period during the live broadcast, and combining the interaction index and preference index for comprehensive analysis to obtain the live broadcast effect feedback index of the current period during the live broadcast; and taking corresponding live broadcast measures for the next period based on the live broadcast effect feedback index of the current period during the live broadcast.

[0007] Further, the user behavior data includes the number of comments, the number of likes, the number of shares, the bullet screen frequency, the number of viewers, the viewing duration, the user churn rate, and the viewing quality.

[0008] Further, the viewing duration is the average viewing duration of each user in the current period during the live broadcast, and the viewing quality is the average resolution of each user's live broadcast viewing.

[0009] Further, the specific steps to obtain the interaction index in the current period during the live broadcast are as follows: normalize the number of comments, the number of likes, the number of shares, and the bullet screen frequency in the current period during the live broadcast; based on the normalized number of comments, the number of likes, the number of shares, and the bullet screen frequency in the current period during the live broadcast, conduct a comprehensive analysis to obtain the interaction index in the current period during the live broadcast.

[0010] Further, the specific steps to obtain the preference index in the current period during the live broadcast are as follows: standardize the number of viewers, the viewing duration, the user churn rate, and the viewing quality in the current period during the live broadcast; based on the standardized number of viewers, the viewing duration, the user churn rate, and the viewing quality in the current period during the live broadcast, conduct a comprehensive analysis to obtain the preference index in the current period during the live broadcast.

[0011] Further, the specific steps to obtain the comment sentiment index in the current period during the live broadcast are as follows: read the text information in the current period during the live broadcast and perform preprocessing; input the preprocessed text information in the current period during the live broadcast into a pre-trained natural language processing model for predictive analysis to obtain the sentiment score of each comment in the current period during the live broadcast; and conduct a comprehensive analysis of the sentiment scores of each comment in the current period during the live broadcast to obtain the comment sentiment index in the current period during the live broadcast.

[0012] Further, the specific steps to take corresponding live broadcast measures for the next period based on the live broadcast effect feedback index in the current period during the live broadcast are as follows: compare the live broadcast effect feedback index in the current period during the live broadcast with a preset live broadcast effect feedback index threshold for judgment and analysis; if the live broadcast effect feedback index in the current period during the live broadcast is lower than the preset live broadcast effect feedback index threshold, take the first live broadcast measure for the next period; if the live broadcast effect feedback index in the current period during the live broadcast is not lower than the preset live broadcast effect feedback index threshold, take the second live broadcast measure for the next period.

[0013] A live broadcast data processing system, comprising: a data acquisition module for acquiring in real time the user behavior data of the current period during the live broadcast; a data analysis module for respectively performing data analysis on the user behavior data of the current period during the live broadcast to obtain an interaction index and a preference index of the current period during the live broadcast; a language comprehensive analysis module for simultaneously acquiring the text information of the current period during the live broadcast, and performing language analysis and processing to obtain a comment sentiment index of the current period during the live broadcast, and combining the interaction index and the preference index for comprehensive analysis to obtain a live broadcast effect feedback index of the current period during the live broadcast; a live broadcast feedback module for taking corresponding live broadcast measures for the next period based on the live broadcast effect feedback index of the current period during the live broadcast.

[0014] The present invention has the following beneficial effects:

[0015] (1) The live broadcast data processing method analyzes by acquiring user behavior data and text information in real time, generates an interaction index, a preference index and a comment sentiment index, so as to realize real-time optimization of the live broadcast content and interaction strategy, so that the live broadcast platform can timely understand the interests, emotional fluctuations and interaction behaviors of the audience in each period, and make a quick response, thereby making the adjustment of the live broadcast content and strategy more flexible and accurate, and then improving the stickiness and activity of the audience, and avoiding the loss of the audience caused by single content or insufficient interaction.

[0016] (2) The live broadcast data processing method generates a live broadcast effect feedback index by comprehensively analyzing the interaction index, the preference index and the comment sentiment index, so as to provide a comprehensive evaluation index, and then make the evaluation of the live broadcast effect more comprehensive and accurate, not only paying attention to the interaction behavior of the audience, but also comprehensively considering its emotional feedback and preference changes, and providing data support for the subsequent live broadcast periods based on the live broadcast effect feedback index, so as to formulate a more targeted live broadcast strategy, thereby effectively improving the user experience, enhancing the sense of participation and satisfaction of the user, and promoting the long-term development of the platform.

[0017] (3) The live broadcast data processing system can accurately evaluate the live broadcast effect in each period through the synergistic effect of the data analysis module, the language comprehensive analysis module and the live broadcast feedback module, and provide data-driven decision support for the live broadcast of the next period, and automatically calculate the live broadcast effect feedback index through comprehensive analysis, so that the live broadcast platform can make accurate content adjustments and interaction strategy optimizations based on real-time feedback, thereby significantly improving the efficiency of live broadcast content adjustment and the real-time nature of decision-making, and then being able to quickly respond to user needs, improve the platform operation efficiency, and reduce the time cost and decision delay of manual intervention.

[0018] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. Brief Description of the Drawings

[0019] Figure 1 This is a flowchart of a method for processing live broadcast data according to the present invention.

[0020] Figure 2 This is a block diagram of a system for processing live broadcast data according to the present invention. Detailed Embodiments

[0021] Please refer to Figure 1 , an embodiment of the present invention provides a technical solution: a method for processing live broadcast data, including the following steps: obtaining user behavior data in the current period (for example: 5 minutes) during the live broadcast in real time; and respectively performing data analysis on the user behavior data in the current period during the live broadcast to obtain an interaction index and a preference index in the current period during the live broadcast; at the same time, obtaining text information in the current period during the live broadcast and performing language analysis and processing to obtain a comment sentiment index in the current period during the live broadcast, and combining the interaction index and the preference index for comprehensive analysis to obtain a live broadcast effect feedback index in the current period during the live broadcast; and taking corresponding live broadcast measures for the next period based on the live broadcast effect feedback index in the current period during the live broadcast.

[0022] The user behavior data includes the number of comments, the number of likes, the number of shares, the barrage frequency, the number of viewers, the viewing duration, the user churn rate, and the viewing quality.

[0023] Among them, the user churn rate is the proportion of viewers leaving the live broadcast room in the current period during the live broadcast.

[0024] The viewing duration is the average value of the viewing duration of each user in the current period during the live broadcast, and the viewing quality is the average value of the resolution of each user watching the live broadcast.

[0025] Specifically, the specific steps for obtaining the interaction index in the current period during the live broadcast are as follows: normalizing the number of comments, the number of likes, the number of shares, and the barrage frequency in the current period during the live broadcast; and performing comprehensive analysis based on the normalized number of comments, the number of likes, the number of shares, and the barrage frequency in the current period during the live broadcast to obtain the interaction index in the current period during the live broadcast.

[0026] The specific steps for obtaining the preference index in the current period during the live broadcast are as follows: standardizing the number of viewers, the viewing duration, the user churn rate, and the viewing quality in the current period during the live broadcast; and performing comprehensive analysis based on the standardized number of viewers, the viewing duration, the user churn rate, and the viewing quality in the current period during the live broadcast to obtain the preference index in the current period during the live broadcast.

[0027] In this implementation, through the normalization of the comment quantity value, like value, share count value, and bullet chat frequency value, and the standardization of the viewership value, viewing duration value, user churn rate value, and viewing quality value, it is ensured that each piece of data has the same scale and dimension, thus avoiding the influence of data value differences on the analysis results. Moreover, normalization and standardization can eliminate the bias caused by different dimensions, ensuring that each indicator obtains a fair weight and influence in the comprehensive analysis, thereby improving the accuracy and consistency of the overall data processing, and ensuring that the calculation results of the interaction index and preference index are more scientific and reliable. Secondly, the data after normalization and standardization can ensure that the influence of each factor on the result is more balanced during the comprehensive analysis, thus avoiding a certain data dominating the analysis result due to its large value, ensuring that different types of user behaviors can be equally considered, and further obtaining more accurate interaction index and preference index.

[0028] Specifically, the specific steps to obtain the comment sentiment index for the current period during the live broadcast are as follows: Read the text information for the current period during the live broadcast and perform preprocessing; Input the preprocessed text information for the current period during the live broadcast into a pre-trained natural language processing model for predictive analysis to obtain the sentiment score for each comment during the current period of the live broadcast; And comprehensively analyze the sentiment scores of each comment during the current period of the live broadcast to obtain the comment sentiment index for the current period during the live broadcast.

[0029] Among them, the natural language processing model is specifically BERT, and BERT includes an input layer, a Transformer encoder layer, and a regression output layer.

[0030] The specific steps to obtain the sentiment score of each comment in the current period during the live broadcast are as follows: In the input layer of BERT, receive the preprocessed text information of the current period during the live broadcast and perform format conversion (tokenization, splitting the text into subwords or chunks and assigning an ID to each subword, and at the same time, adding special tokens [CLS] and [SEP] to mark the start of the text and separate different sentences; embedding, converting the tokenized text into vector form, including word embedding, mapping each subword ID to a vector of a fixed dimension; position embedding, providing position information for each word to handle the word order problem; segment embedding, if the input contains multiple sentences, using segment embedding to distinguish different sentences); in the Transformer encoder layer of BERT, perform self-attention processing on the preprocessed text information of the current period during the live broadcast (self-attention mechanism, by calculating the correlation between each word and other words, the model can consider other words in the sequence when processing a word, enhance the context understanding, and use multiple "heads" to capture different context information in parallel, each head learning the semantics of the input text from different perspectives, and finally fusing this information through weighted summation, and after each layer of Transformer, there is a feed-forward neural network to further process the context information and help learn complex non-linear relationships), to obtain the text vector of the current period during the live broadcast; in the regression output layer of BERT, perform predictive analysis on the text vector of the current period during the live broadcast (after being processed by the Transformer encoder of BERT, the vector corresponding to the [CLS] token of each comment is used as the overall representation of the comment, containing context information, and input the [CLS] vector of each comment into the regression output layer, and the regression layer processes this vector through a fully connected layer, that is, a linear transformation, and outputs a numerical value, usually corresponding to the sentiment score of the comment, and finally outputs the sentiment score, usually a continuous numerical value representing the sentiment intensity or polarity, and the range may be from -1 to 1, representing negative to positive sentiment), to obtain the sentiment score of each comment in the current period during the live broadcast.

[0031] In this implementation, by using the BERT model, especially its powerful Transformer encoder layer and self-attention mechanism, fully considering the context information of each comment, capturing the complex relationships between words and between sentences, thus being able to simultaneously focus on the relevance of the entire sentence or multiple sentences, thereby improving the accuracy of sentiment analysis, and then enhancing the prediction accuracy of the sentiment score. At the same time, in the Transformer encoder layer of BERT, the model captures different context information in parallel through multiple "heads", thus being able to learn the semantics of the input text from multiple perspectives, and finally fusing this information through weighted summation to generate a more comprehensive text representation, so as to make accurate judgments on the subtle tones and sentiment fluctuations in the comments.

[0032] Specifically, the specific steps of taking corresponding live broadcast measures for the next period based on the live broadcast effect feedback index in the current period during the live broadcast are as follows: Judge and analyze the live broadcast effect feedback index in the current period during the live broadcast with the preset live broadcast effect feedback index threshold; If the live broadcast effect feedback index in the current period during the live broadcast is lower than the preset live broadcast effect feedback index threshold, take the first live broadcast measure for the next period (provide relevant personnel with suggestions for adjusting the live broadcast content, that is, adding more interesting interactive sessions or introducing fresh topics; enhancing interactivity, such as asking questions, holding lotteries, answering questions, etc., to encourage the audience to participate and improve their activity; optimizing the performance of the host, such as adjusting the tone, rhythm or interaction method to enhance the viewing experience of the audience); If the live broadcast effect feedback index in the current period during the live broadcast is not lower than the preset live broadcast effect feedback index threshold, take the second live broadcast measure for the next period (provide relevant personnel with suggestions for maintaining content stability, continuing to maintain the current live broadcast theme and form; improving the viewing quality, optimizing the live broadcast quality, improving the video resolution, fluency, etc.; enhancing subsequent interactivity, such as setting up more interactive sessions in advance to maintain the enthusiasm of the audience to participate).

[0033] In this implementation plan, through the dynamic adjustment of the live broadcast content for the next period based on the live broadcast effect feedback index, the live broadcast effect is optimized in real time, thereby enhancing the audience's sense of participation and satisfaction, and timely identifying and taking measures when the live broadcast effect is poor, such as adding interactive sessions, adjusting content or optimizing the performance of the host, thereby enhancing the audience's participation and platform stickiness. For the situation with a higher feedback index, the second live broadcast measure ensures content coherence and stability, avoids excessive interference with the audience experience, maintains the viewing quality, and improves user satisfaction. Finally, through data-driven decision-making, the platform can accurately evaluate the live broadcast effect and optimize the strategy, avoiding subjective judgment, thereby enhancing the accuracy and efficiency of the live broadcast content, further improving the timeliness and pertinence of content optimization, and then enhancing the operation efficiency of the platform.

[0034] Please refer to Figure 2 , an embodiment of the present invention provides a technical solution: A live broadcast data processing system, including: A data acquisition module, used to acquire the user behavior data in the current period during the live broadcast in real time; A data analysis module, used to perform data analysis on the user behavior data in the current period during the live broadcast respectively to obtain the interaction index and preference index in the current period during the live broadcast; A language comprehensive analysis module, used to simultaneously acquire the text information in the current period during the live broadcast and perform language analysis and processing to obtain the comment sentiment index in the current period during the live broadcast, and perform comprehensive analysis in combination with the interaction index and preference index to obtain the live broadcast effect feedback index in the current period during the live broadcast; A live broadcast feedback module, used to take corresponding live broadcast measures for the next period based on the live broadcast effect feedback index in the current period during the live broadcast.

[0035] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn of the basic inventive concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention.

[0036] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A live broadcast data processing method, characterized in that, It includes the following steps: Obtain the user behavior data of the current period during the live broadcast in real time; And respectively perform data analysis on the user behavior data of the current period during the live broadcast to obtain the interaction index and preference index of the current period during the live broadcast; At the same time, obtain the text information of the current period during the live broadcast, and perform language analysis and processing to obtain the comment sentiment index of the current period during the live broadcast, and combine the interaction index and preference index for comprehensive analysis to obtain the live broadcast effect feedback index of the current period during the live broadcast; And take corresponding live broadcast measures for the next period based on the live broadcast effect feedback index of the current period during the live broadcast.

2. The live broadcast data processing method according to claim 1, wherein The user behavior data includes the comment quantity value, like value, share times value, bullet screen frequency value, number of viewers value, viewing duration value, user churn rate value, and viewing quality value.

3. The live data processing method according to claim 2, wherein The viewing duration value is the average value of the viewing duration of each user in the current period during the live broadcast, and the viewing quality value is the average value of the resolution of each user watching the live broadcast.

4. The live broadcast data processing method according to claim 2, characterized in that, The specific steps to obtain the interaction index of the current period during the live broadcast are as follows: Normalize the comment quantity value, like value, share times value, and bullet screen frequency value of the current period during the live broadcast; Based on the comment quantity value, like value, share times value, and bullet screen frequency value of the current period during the live broadcast after normalization processing, perform comprehensive analysis to obtain the interaction index of the current period during the live broadcast.

5. The live broadcast data processing method according to claim 2, characterized in that, The specific steps to obtain the preference index of the current period during the live broadcast are as follows: Standardize the number of viewers value, viewing duration value, user churn rate value, and viewing quality value of the current period during the live broadcast; Based on the number of viewers value, viewing duration value, user churn rate value, and viewing quality value of the current period during the live broadcast after standardization processing, perform comprehensive analysis to obtain the preference index of the current period during the live broadcast.

6. The live broadcast data processing method according to claim 1, wherein, The specific steps to obtain the comment sentiment index of the current period during the live broadcast are as follows: Read the text information of the current period during the live broadcast and perform preprocessing; Input the preprocessed text information of the current period during the live broadcast into a pre-trained natural language processing model for predictive analysis to obtain the sentiment score of each comment of the current period during the live broadcast; And perform comprehensive analysis on the sentiment scores of each comment of the current period during the live broadcast to obtain the comment sentiment index of the current period during the live broadcast.

7. The live broadcast data processing method according to claim 1, wherein The specific steps to take corresponding live broadcast measures for the next period based on the live broadcast effect feedback index of the current period during the live broadcast are as follows: Judge and analyze the live broadcast effect feedback index of the current period during the live broadcast with a preset live broadcast effect feedback index threshold; If the live broadcast effect feedback index of the current period during the live broadcast is lower than the preset live broadcast effect feedback index threshold, take the first live broadcast measure for the next period; If the live broadcast effect feedback index of the current period during the live broadcast is not lower than the preset live broadcast effect feedback index threshold, take the second live broadcast measure for the next period.

8. A live broadcast data processing system, which applies the live broadcast data processing method described in any one of claims 1-7, characterized in that, It includes: A data acquisition module for obtaining the user behavior data of the current period during the live broadcast in real time; The data analysis module is used to perform data analysis on the user behavior data in the current period during the live broadcast respectively, and obtain the interaction index and preference index in the current period during the live broadcast; The language comprehensive analysis module is used to simultaneously obtain the text information in the current period during the live broadcast, and perform language analysis and processing to obtain the comment sentiment index in the current period during the live broadcast, and conduct comprehensive analysis in combination with the interaction index and preference index to obtain the live broadcast effect feedback index in the current period during the live broadcast; The live broadcast feedback module is used to take corresponding live broadcast measures for the next period based on the live broadcast effect feedback index in the current period during the live broadcast.

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