Topic Sentiment Analysis in Online Learning Community from College Students

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A proposed method algorithm for calculating sentiment scores matrix_

Input:	A topic formal context K=(U, T, I), where U={u1,u2,…,un} represents a set of topics belonging to a group of students, T={t1,t2,…,tm}, n is the size of student set, m is the size of topics.
Output:	A sentiment scores matrix Sentimentscore(ti), where irepresents sentiment score of each topic.
1.	for each topic ti in T.
2.	Sentimentscore(ti)=0.
3.	P(ti, ui)=0.
4.	Derive the positive and negative seed terms on the basis of domain experts.
5.	Compute simKL(ti, uj) // Compute the mutual information.
6.	Compute SD(ti, tseed) // Compute the sentiment comprehensive value.
7.	for each topic of student uj in the topic formal context K.
8.	SD(ti, uj)=0.
9.	for each topic of ti in the topic formal context K.
10.	SD(ti, uj)= SD(ti, tseed)+ SD(uj, tseed).
11.	end for.
12.	Sentimentscore(ti)= Sentimentscore(ti)+ SD(ti, uj).
13.	end for.
14.	end for.
15.	Return Sentimentscore(ti).

The binary sentiment of the single-valued formal context_

	T1	T2	T3	T4	T5	T6	T7	T8	T9	T10	T11	T12	T13	T14	T15	T16	T17	T18	T19	T20
D1	*			*			*	*		*			*				*	*
D2			*		*	*			*	*		*		*	*					*
D3				*			*	*			*		*		*			*	*
D4	*				*	*		*			*	*	*		*
D5		*			*	*			*	*		*		*			*			*
D6		*			*	*		*			*		*				*	*
D7			*		*		*	*			*			*		*				*
D8	*				*		*	*			*	*	*		*			*
D9	*				*	*		*		*			*		*					*
D10			*		*	*		*		*		*	*	*	*			*

A proposed method algorithm for topic-clustered concept lattice generation_

Input:	A set of topic and comment documentation D, where | D |=n, the number of potential topics m.
Output:	A topic-clustered concept lattice CL, a topics-terms probability matrix P and a documents-topics probability matrix R.
1.	for each di ∈ D.
2.	di → CWSi. // Convert the document into a word segment.
3.	for each cws in CWSi.
4.	W = W ∪ {cws}. // Obtain a collection of phrases that contains topic attribute.
5.	end for.
6.	end for.
7.	for each cws in CWSi.
8.	CWSi → tfidfi. // Calculate the term frequency of attributes.
9.	D‘=[D : tfidfi]\mathop D\limits^{'} = [D\,\,:\,{tfidf}_i] . // Obtain term frequency vector.
10.	end for.
11.	(D‘,W)→LDA(D,P,I)(\mathop D\limits^{'} ,W)\buildrel {LDA} \over \longrightarrow (D,P,I) . // Perform topic detection.
12.	(D‘, P)→R(\mathop D\limits^{'} ,\,P) \to R . // Classify topic association matrix.
13.	Find the subset of topic attributes represented as tj.
14.	for j=1 to 2m.
15.	Compute the set of objects by applying the Glois connection.
16.	R → I′. // Convert topic association matrix to multi-valued formal context.
17.	I′ → I. // Convert multi-valued formal context to binary single-valued formal context.
18.	(D‘,R,I')→FCACL(D,R,I')(\mathop D\limits^{'} ,R,I')\buildrel {FCA} \over \longrightarrow CL(D,R,I') . // Construct a hierarchical topic concept lattice.
19.	end for.
20.	Return {CL( D‘\mathop D\limits^{'} , R, I′), P, R}.
21.	Derive the topic-clustered sets.

Classification weights for adverb of degree_

Level(weights)	Included adverbs
adv1(1.5)	excessively, completely, extensively, dreadfully, entirely, absulutely
adv2(1.3)	fairly, pretty, rather, quite, very, much, greatly, by far, hightly, deeply
adv3(1.1)	really, almost, nearly, bven, just, still
adv4(1)	slightly, a little, a bit, trifle, somewhat

Multi-valued sentiment formal context based on topic association matrix_

	T1	T2	T3	T4
D1	−3.427	2.874	4.315	−1.306
D2	2.641	−0.597	−2.105	2.635
D3	4.715	2.132	1.624	0
D4	2.334	0	−1.748	4.316
D5	−3.619	−1.857	3.624	−0.391
D6	−2.107	2.167	2.419	2.361
D7	0	−0.524	−0.267	2.638
D8	2.369	1.629	2.364	0
D9	1.024	−0.121	3.478	2.964
D10	2.361	1.493	−0.328	−1.267

The implication rules and association rules_

Association rules	1<3>Learner Information provider<AVG NT2=[100%]=><3>Information searcher>AVG;
	2<4>Learner Psychological stress PT1=[75%]=><3>Information provider<AVG NT3;
	3<4>Learner NT2 =[75%]=><3>Interaction;
	4<4>Learner NT2 =[75%]=><3>Information sharer>AVG Information searcher>AVG;
	5<3>Learner Information sharer<AVG Psychological stress Cooperation PT1=[67%]=><2> Information provider<AVG NT3;
	6<3>Learner Information searcher>AVG Psychological stress PT1 NT3 =[67%]=><2> Postgraduate Information searcher<AVG Interaction;
	7<3>Learner Information provider<AVG PT1 PT4=[67%]=><2>Information searcher<AVG NT2;
	8<3> Learner Information provider<AVG Information searcher<AVG NT2=[67%]=><2> Information sharer>AVG Psychological stress Interaction;
	9<3>Learner NT2 PT4 =[67%]=><2>Postgraduate Interaction;
	10<3>Learner NT2 PT4 =[67%]=><2>Information sharer<AVG;
Implication rules	1<2>Learner Information sharer>AVGInteraction cooperation ==> Information searcher>AVG Psychological stress PT2;
	2<2>Learner Interaction sharer>AVG NT3==> Information searcher<AVG Psychological stress;
	3<2>Learner Information searcher>AVG Interaction cooperation ==> Information sharer>AVG Psychological stress PT4;

Recognition results of topic terms_

Topic	Term and its probability
T1	Course selection/0.023, Learning objectives/0.021, Difficulty of knowledge/0.018, Teaching methods/0.017, Guidance methods/0.013
T2	Credits/0.025, Content organization/0.023, Teaching methods/0.021, Learning support/0.021, Homework and assessment methods/0.020
T3	Case presentation/0.032, Procedural evaluation/0.031, Knowledge expansion/0.029, Analysis of difficult points/0.027, Group discussion/0.027
T4	Communication and feedback/0.033, Resource sharing/0.033, Information update/0.032, Response time/0.031, Information acceptance/0.030

Precision contrast between different methods based on SVM_

	St1	St2	St3	St4	St5	St6	St7
RA	49.32	37.51	40.67	42.52	43.77	41.26	45.33
CG	52.33	34.96	38.79	41.68	40.17	37.74	42.59
CoT	57.73	46.28	48.85	44.84	51.39	47.77	48.25
TextBlob	58.86	45.16	46.07	42.33	52.78	45.56	52.63
TSAOLC	61.34	50.23	54.95	49.83	53.95	62.98	54.36

MAE contrast between different methods based on SVM_

	St1	St2	St3	St4	St5	St6	St7
RA	98.42	92.46	90.87	88.38	89.07	91.45	95.63
CG	82.03	85.56	87.69	89.06	92.61	94.97	86.36
CoT	78.84	76.34	72.19	68.78	75.43	76.35	78.62
TextBlob	72.93	67.45	69.37	64.92	70.14	68.62	62.15
TSAOLC	58.99	54.56	57.32	55.25	57.20	59.15	53.13

Recall contrast between different methods based on SVM_

	St1	St2	St3	St4	St5	St6	St7
RA	44.45	42.06	47.64	44.37	45.98	41.63	48.21
CG	42.68	40.97	48.86	42.07	43.63	42.88	47.71
CoT	49.99	47.38	52.84	55.36	52.09	49.23	53.84
TextBlob	54.18	45.84	51.67	58.07	62.29	53.46	60.06
TSAOLC	56.49	58.03	62.27	59.96	65.59	58.76	62.34

F-measure contrast between different methods based on SVM_

	St1	St2	St3	St4	St5	St6	St7
RA	46.67	39.65	43.88	43.43	44.85	41.44	46.73
CG	47.01	37.73	43.25	41.87	41.83	40.15	45.00
CoT	53.58	46.82	50.77	49.55	51.74	48.49	50.89
TextBlob	56.42	45.50	48.71	48.97	57.14	49.19	56.10
TSAOLC	58.82	53.85	59.38	54.43	59.20	60.80	58.08