CN117235244B - Online course learning emotion experience evaluation system based on barrage emotion word classification - Google Patents
Online course learning emotion experience evaluation system based on barrage emotion word classification Download PDFInfo
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Abstract
The invention discloses an online course learning emotion experience evaluation system based on barrage emotion word classification, which comprises: bullet screen acquisition module: the bullet screen data acquisition module is used for acquiring bullet screen data; the emotion judging module is used for: carrying out emotion judgment on bullet screen data to obtain emotion judgment results; emotion analysis module: analyzing according to the emotion judgment result to obtain an emotion difference coefficient, an emotion influence coefficient and an emotion difference condition in a period of time; the emotion inspection module is used for carrying out secondary inspection on bullet screen data meeting secondary inspection conditions; and a result visualization module: and carrying out data visualization analysis on the emotion judgment result, the emotion difference coefficient and the emotion influence coefficient to generate an emotion analysis report. Through the mode, the bullet screen word segmentation is more accurate; particularly, emotion judgment of texts such as network expression, positive and negative words and the like in the bullet screen is more accurate; the emotion grasping method is beneficial to teachers to grasp emotion of the whole course and improves teaching design more pertinently.
Description
Technical Field
The invention relates to the technical field of text emotion analysis, in particular to an online course learning emotion experience evaluation system based on barrage emotion word classification.
Background
The development of digital technologies such as the Internet, big data and the like provides powerful technical support for the continuous promotion of digital transformation strategy of education in China. The online course is developed for more than ten years from the original year of the lesson in 2012, and is one of the most popular learning modes of the instructor and the learner in the education field, and the online course is the main mode of teaching various schools in all levels of China because of the unique advantages of breaking through space-time limitation. However, the drawbacks of emotional absence are also emerging as the engineers are unable to perceive each other's emotional condition during online lessons. Therefore, the emotion experience of the students in the online courses is analyzed, so that teachers can master various emotion experiences of learners in the learning process as soon as possible, and the teaching design of the online courses is improved based on emotion analysis results.
Bulletin number CN 110569354B, named barrage emotion analysis method and device, which highlights how to match barrage texts with emotion dictionaries, so that obtained emotion words are vectorized to input emotion analysis models to obtain emotion types of barrage texts; the method comprises the steps of extracting comment texts of each course, and carrying out word frequency statistics on words in the comment texts to obtain high-frequency words as keywords, wherein the publication number is CN 116011856A; carrying out emotion orientation analysis on the keywords to obtain emotion orientation evaluation of courses and outputting the emotion orientation evaluation; the invention carries out subsection discussion on the learning video, calculates the emotion difference and the influence degree of the emotion difference on the barrage data in each section of learning video so as to know the influence of the barrage data on barrage interaction in the current time period, and carries out secondary inspection if necessary so as to output an emotion analysis report.
Disclosure of Invention
The invention mainly solves the technical problem of providing an online course learning emotion experience evaluation system based on barrage emotion word classification, which can carry out emotion polarity and emotion analysis on barrage data accurately, so that teachers can grasp the emotion of the whole course more accurately, and the teaching design is improved more pertinently.
In order to solve the technical problems, the invention adopts a technical scheme that: the utility model provides an online course study emotion experience evaluation system based on barrage emotion word classification, the system includes:
bullet screen acquisition module: writing a barrage acquisition program by using Python, and acquiring through an interface provided by a website to obtain barrage data in a learning video;
the emotion judging module is used for: the method is used for carrying out emotion judgment on the bullet screen data to obtain emotion judgment results; the emotion judging module comprises: the bullet screen classifying module and the bullet screen judging module;
emotion analysis module: analyzing according to the emotion judgment result to obtain an emotion difference coefficient, an emotion influence coefficient and an emotion difference condition in a period of time;
emotion checking module: judging whether the emotion difference coefficient meets a secondary test condition, if so, performing secondary test on the barrage data and transferring to an emotion judgment module, otherwise, directly transferring to a result visualization module;
and a result visualization module: carrying out data visualization analysis on the emotion judgment result, the emotion difference coefficient and the emotion influence coefficient to generate an emotion analysis report;
the bullet screen data comprises: speaker id, time of speaking, bullet screen text;
the emotion analysis module specifically comprises:
s1: the video segmentation time setting module is used for setting video segmentation time;
s2: the video segmentation module is used for segmenting the learning video into a plurality of segmented videos according to the video segmentation time;
s3: the emotion difference condition coefficient calculation module is used for constructing an emotion difference formula, calculating emotion difference coefficients of the segmented video according to emotion assignment and analyzing emotion difference conditions;
s4: the emotion difference influence coefficient calculation module is used for constructing an emotion difference influence formula and calculating according to emotion assignment to obtain an emotion difference influence coefficient;
the emotion difference formula is calculated as follows:
;
wherein K represents an emotion difference coefficient;representing positive emotion sum, & lt & gt>Representing the sum of negative emotions, & lt & gt>Represents a neutral emotion sum.
Further, the barrage classification module classifies the barrage data according to the barrage text to obtain barrage data classification results;
the barrage data classification result comprises: plain text barrage, text-symbol barrage, and plain symbol barrage.
Further, the barrage judging module judges emotion polarity and performs emotion statistics on the barrage data classification result through an emotion dictionary;
the emotion dictionary comprises: an emotion polarity dictionary database and a symbol dictionary database;
the emotion statistics means that the barrage text is compared with the emotion dictionary, if emotion words or emotion symbols are contained in the barrage text, emotion polarity is judged, and the sum of all emotion polarities is counted;
the emotion polarity total amount includes: positive emotion sum, negative emotion sum, neutral emotion sum.
Further, the emotion judgment result includes: speaker id, talk time, barrage text, and emotion polarity sum.
Further, the emotion difference condition carries out emotion difference judgment according to the emotion difference coefficient, and specifically includes:
if the emotion difference coefficient tends to 0, the larger emotion difference of the barrage is indicated;
when the emotion difference coefficient K is more than 0, the emotion barrage is mainly positive emotion, and when K tends to be 1, the difference of the emotion barrage is smaller;
and when the emotion difference coefficient K is smaller than 0, the emotion barrage is mainly negative emotion, and when K tends to be-1, the difference of the emotion barrage is smaller.
Further, the emotion difference affects a formula, and a calculation formula is as follows:
;
wherein W represents an affective difference influence coefficient; if W tends to 1, the influence of emotion difference on barrage emotion interaction of the current segmented video is large; if W tends to 0, the influence of emotion difference on barrage emotion interaction of the current segmented video is small.
Further, the secondary test condition is that the absolute value of the emotion difference coefficient is compared with a test threshold value, if the absolute value of the emotion difference coefficient is larger than the test threshold value, the secondary test is performed, and otherwise, the secondary test is not performed;
the inspection threshold value is combined with teacher experience and the learning video setting;
and the secondary test is carried out, manual marking is carried out on the barrage data, and judgment of emotion polarity and emotion statistics are carried out.
Further, the result visualization module automatically generates an emotion analysis report according to the emotion detection result, the emotion judgment result and the emotion difference condition;
the emotion analysis report is a part for displaying the overall emotion distribution of the barrage in the learning video and presenting the emotion difference with large emotion difference in the barrage data in the learning video.
The beneficial effects of the invention are as follows: (1) the real-time barrage collection of the online courses is more efficient, and barrage word segmentation is more accurate;
(2) the emotion analysis of the barrage text is more accurate, and particularly, the emotion judgment of the text such as network expression, positive and negative language in the barrage is more accurate;
(3) the visual system is beneficial to the teacher to grasp the emotion of the whole course, and the teaching design is improved more pertinently.
Drawings
FIG. 1 is a flow chart of an online course learning emotion experience evaluation system based on barrage emotion word classification of the present invention;
FIG. 2 is a flow diagram of a barrage acquisition module of the online course learning emotion experience evaluation system based on barrage emotion word classification;
FIG. 3 is a schematic diagram of emotion difference conditions of an online course learning emotion experience evaluation system based on barrage emotion word classification.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making clear and defining the scope of the present invention.
Referring to fig. 1, 2 and 3, an embodiment of the present invention includes:
an online course learning emotion experience evaluation system based on barrage emotion word classification, the system comprising:
bullet screen acquisition module: writing a barrage acquisition program by using Python, and acquiring through an interface provided by a website to obtain barrage data in a learning video;
the emotion judging module is used for: the method is used for carrying out emotion judgment on the bullet screen data to obtain emotion judgment results; the emotion judging module comprises: the bullet screen classifying module and the bullet screen judging module;
emotion analysis module: analyzing according to the emotion judgment result to obtain an emotion difference coefficient, an emotion influence coefficient and an emotion difference condition in a period of time;
emotion checking module: judging whether the emotion difference coefficient meets a secondary test condition, if so, performing secondary test on the barrage data and transferring to an emotion judgment module, otherwise, directly transferring to a result visualization module;
and a result visualization module: carrying out data visualization analysis on the emotion judgment result, the emotion difference coefficient and the emotion influence coefficient to generate an emotion analysis report;
the bullet screen data comprises: speaker id, time of speaking, bullet screen text;
the emotion analysis module specifically comprises:
s1: the video segmentation time setting module is used for setting video segmentation time;
s2: the video segmentation module is used for segmenting the learning video into a plurality of segmented videos according to the video segmentation time;
s3: the emotion difference condition coefficient calculation module is used for constructing an emotion difference formula, calculating emotion difference coefficients of the segmented video according to emotion assignment and analyzing emotion difference conditions;
s4: the emotion difference influence coefficient calculation module is used for constructing an emotion difference influence formula and calculating according to emotion assignment to obtain an emotion difference influence coefficient;
the emotion difference formula is calculated as follows:
;
wherein K represents an emotion difference coefficient;representing positive emotion sum, & lt & gt>Representing the sum of negative emotions, & lt & gt>Represents a neutral emotion sum.
As shown in fig. 2, the barrage acquisition module specifically includes:
inputting a video website;
acquiring bullet screen data;
analyzing bullet screen information;
generating a barrage text;
and outputting the barrage text to a emotion judging module.
Further, the barrage classification module classifies the barrage data according to the barrage text to obtain barrage data classification results;
the barrage data classification result comprises: plain text barrage, text-symbol barrage, and plain symbol barrage.
Further, the barrage judging module judges emotion polarity and performs emotion statistics on the barrage data classification result through an emotion dictionary;
the emotion dictionary comprises: an emotion polarity dictionary database and a symbol dictionary database;
the emotion statistics means that the barrage text is compared with the emotion dictionary, if emotion words or emotion symbols are contained in the barrage text, emotion polarity is judged, and the sum of all emotion polarities is counted;
the emotion polarity total amount includes: positive emotion sum, negative emotion sum, neutral emotion sum.
Further, the emotion judgment result includes: speaker id, talk time, barrage text, and emotion polarity sum.
Further, the emotion difference condition carries out emotion difference judgment according to the emotion difference coefficient, and specifically includes:
if the emotion difference coefficient tends to 0, the larger emotion difference of the barrage is indicated;
when the emotion difference coefficient K is more than 0, the emotion barrage is mainly positive emotion, and when K tends to be 1, the difference of the emotion barrage is smaller;
and when the emotion difference coefficient K is smaller than 0, the emotion barrage is mainly negative emotion, and when K tends to be-1, the difference of the emotion barrage is smaller.
TABLE 1 emotional disparity Condition for segmented video
| Time period/min | Positive emotion sum/bar | Sum/bar of negative emotions | Neutral emotion sum/bar | K value |
| 0-1 | 22 | 15 | 26 | 0.1871 |
| 1-2 | 4 | 5 | 6 | -0.1064 |
| 11-12 | 0 | 0 | 4 | 0.0000 |
As shown in table 1 and fig. 3, the segmentation time of the learning video is set to be 1min, the positive emotion sum is 22, the negative emotion sum is 15, the neutral emotion sum is 26, the emotion difference coefficient K obtained by calculation is 0.1871, the learning video is 0-1min, the positive emotion is mainly, and the emotion difference is large.
Further, the emotion difference affects a formula, and a calculation formula is as follows:
;
wherein W represents an affective difference influence coefficient; if W tends to 1, the influence of emotion difference on barrage emotion interaction of the current segmented video is large; if W tends to 0, the influence of emotion difference on barrage emotion interaction of the current segmented video is small.
TABLE 2 Emotion Difference coefficient for segmented video
| Time period/min | Positive emotion sum/bar | Sum/bar of negative emotions | Neutral emotion sum/bar | W value |
| 0-1 | 22 | 15 | 26 | 0.5873 |
| 1-2 | 4 | 5 | 6 | 0.6000 |
| 11-12 | 0 | 0 | 4 | 0.0000 |
As shown in table 2, the segmentation time of the learning video was set to 1min, and in the first segmented video, the emotion difference influence coefficient was 0.5873, and in the second segmented video, the emotion difference influence coefficient was 0.6000, thereby explaining: in contrast, the affective differences of the second segmented video have a greater impact on the overall bullet screen.
Further, the secondary test condition is that the absolute value of the emotion difference coefficient is compared with a test threshold value, if the absolute value of the emotion difference coefficient is larger than the test threshold value, the secondary test is performed, and otherwise, the secondary test is not performed;
the test threshold value is set to 0.8 by the current video in combination with the experience of a teacher and the learning video setting;
and the secondary test is carried out, manual marking is carried out on the barrage data, and judgment of emotion polarity and emotion statistics are carried out.
Further, the result visualization module automatically generates an emotion analysis report according to the emotion judgment result and the emotion difference condition;
the emotion analysis report is a part for displaying the overall emotion distribution of the barrage in the learning video and presenting the emotion difference with large emotion difference in the barrage data in the learning video.
The foregoing description is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent structures or equivalent processes or direct or indirect application in other related technical fields are included in the scope of the present invention.
Claims (6)
1. An online course learning emotion experience evaluation system based on barrage emotion word classification, the system comprising:
bullet screen acquisition module: writing a barrage acquisition program by using Python, and acquiring through an interface provided by a website to obtain barrage data in a learning video;
the emotion judging module is used for: the method is used for carrying out emotion judgment on the bullet screen data to obtain emotion judgment results; the emotion judging module comprises: the bullet screen classifying module and the bullet screen judging module;
emotion analysis module: analyzing according to the emotion judgment result to obtain an emotion difference coefficient, an emotion influence coefficient and an emotion difference condition in a period of time;
emotion checking module: judging whether the emotion difference coefficient meets a secondary test condition, if so, performing secondary test on the barrage data and transferring to an emotion judgment module, otherwise, directly transferring to a result visualization module;
and a result visualization module: carrying out data visualization analysis on the emotion judgment result, the emotion difference coefficient and the emotion influence coefficient to generate an emotion analysis report;
the bullet screen data comprises: speaker id, time of speaking, bullet screen text;
the barrage classification module classifies the barrage data according to the barrage text to obtain barrage data classification results;
the barrage judging module judges emotion polarity and performs emotion statistics on the barrage data classification result through an emotion dictionary;
the emotion analysis module specifically comprises:
s1: the video segmentation time setting module is used for setting video segmentation time;
s2: the video segmentation module is used for segmenting the learning video into a plurality of segmented videos according to the video segmentation time;
s3: the emotion difference condition coefficient calculation module is used for constructing an emotion difference formula, calculating emotion difference coefficients of the segmented video according to emotion assignment and analyzing emotion difference conditions;
s4: the emotion difference influence coefficient calculation module is used for constructing an emotion difference influence formula and calculating according to emotion assignment to obtain an emotion difference influence coefficient;
the emotion difference formula is calculated as follows:
;
wherein K represents an emotion difference coefficient;representing positive emotion sum, & lt & gt>Representing the sum of negative emotions,Representing a neutral emotion sum;
the emotion difference affects the formula, and the calculation formula is as follows:
wherein W represents an affective difference influence coefficient; if W tends to 1, the influence of emotion difference on barrage emotion interaction of the current segmented video is large; if W tends to 0, the influence of emotion difference on barrage emotion interaction of the current segmented video is small;
the secondary test condition is that the absolute value of the emotion difference coefficient is compared with a test threshold value, if the absolute value of the emotion difference coefficient is larger than the test threshold value, the secondary test is performed, and otherwise, the secondary test is not performed;
the inspection threshold value is combined with teacher experience and the learning video setting;
and the secondary test is carried out, manual marking is carried out on the barrage data, and judgment of emotion polarity and emotion statistics are carried out.
2. The online lesson learning emotion experience assessment system based on barrage emotion word classification of claim 1, wherein the barrage data classification result comprises: plain text barrage, text-symbol barrage, and plain symbol barrage.
3. The online lesson learning emotion experience assessment system based on barrage emotion word classification of claim 1, wherein the emotion dictionary comprises: an emotion polarity dictionary database and a symbol dictionary database;
the emotion statistics means that the barrage text is compared with the emotion dictionary, if emotion words or emotion symbols are contained in the barrage text, emotion polarity is judged, and the sum of all emotion polarities is counted;
the emotion polarity sum includes: positive emotion sum, negative emotion sum, neutral emotion sum.
4. The online lesson learning emotion experience assessment system based on barrage emotion word classification of claim 1, wherein the emotion judgment result comprises: speaker id, talk time, barrage text, and emotion polarity sum.
5. The online course learning emotion experience evaluation system based on barrage emotion word classification of claim 1, wherein the emotion difference condition performs emotion difference judgment according to the emotion difference coefficient, and specifically comprises:
if the emotion difference coefficient tends to 0, the larger emotion difference of the barrage is indicated;
when the emotion difference coefficient K is more than 0, the emotion barrage is mainly positive emotion, and when K tends to be 1, the difference of the emotion barrage is smaller;
and when the emotion difference coefficient K is smaller than 0, the emotion barrage is mainly negative emotion, and when K tends to be-1, the difference of the emotion barrage is smaller.
6. The online course learning emotion experience evaluation system based on barrage emotion word classification of claim 1, wherein the result visualization module automatically generates an emotion analysis report according to the emotion judgment result and emotion difference condition;
and the emotion analysis report is used for carrying out data display on the overall emotion distribution of the barrage in the learning video and presenting a part with big emotion difference in the barrage data in the learning video.
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