feat(analyze): 添加情感分析功能并优化新闻推送通知

- 在 App.vue 中添加情感分析相关的导入和使用
- 在 app_common.go 中实现 AnalyzeSentiment 方法- 在 market_news_api.go 和 models.go 中集成情感分析结果
- 更新前端通知显示，根据情感分析结果调整通知类型和样式
- 在 go.mod 中添加 gojieba 依赖用于情感分析

app_common.go

@@ -27,3 +27,7 @@ func (a *App) IndustryResearchReport(industryCode string) []any {
 func (a App) EMDictCode(code string) []any {
 	return data.NewMarketNewsApi().EMDictCode(code, a.cache)
 }
+
+func (a App) AnalyzeSentiment(text string) data.SentimentResult {
+	return data.AnalyzeSentiment(text)
+}

backend/data/market_news_api.go

@@ -73,6 +73,7 @@ func (m MarketNewsApi) GetNewTelegraph(crawlTimeOut int64) *[]models.Telegraph {
 
 		//telegraph = append(telegraph, ReplaceSensitiveWords(selection.Text()))
 		if telegraph.Content != "" {
+			telegraph.SentimentResult = AnalyzeSentiment(telegraph.Content).Description
 			cnt := int64(0)
 			db.Dao.Model(telegraph).Where("time=? and source=?", telegraph.Time, telegraph.Source).Count(&cnt)
 			if cnt == 0 {
@@ -192,6 +193,7 @@ func (m MarketNewsApi) GetSinaNews(crawlTimeOut uint) *[]models.Telegraph {
 			logger.SugaredLogger.Infof("telegraph.SubjectTags:%v %s", telegraph.SubjectTags, telegraph.Content)
 
 			if telegraph.Content != "" {
+				telegraph.SentimentResult = AnalyzeSentiment(telegraph.Content).Description
 				cnt := int64(0)
 				db.Dao.Model(telegraph).Where("time=? and source=?", telegraph.Time, telegraph.Source).Count(&cnt)
 				if cnt == 0 {

backend/data/stock_sentiment_analysis.go

@@ -0,0 +1,282 @@
+package data
+
+import (
+	"bufio"
+	"fmt"
+	"github.com/yanyiwu/gojieba"
+	"os"
+	"strings"
+)
+
+// 金融情感词典，包含股票市场相关的专业词汇
+var (
+	// 正面金融词汇及其权重
+	positiveFinanceWords = map[string]float64{
+		"上涨": 2.0, "涨停": 3.0, "牛市": 3.0, "反弹": 2.0, "新高": 2.5,
+		"利好": 2.5, "增持": 2.0, "买入": 2.0, "推荐": 1.5, "看多": 2.0,
+		"盈利": 2.0, "增长": 2.0, "超预期": 2.5, "强劲": 1.5, "回升": 1.5,
+		"复苏": 2.0, "突破": 2.0, "创新高": 3.0, "回暖": 1.5, "上扬": 1.5,
+		"利好消息": 3.0, "收益增长": 2.5, "利润增长": 2.5, "业绩优异": 2.5,
+		"潜力股": 2.0, "绩优股": 2.0, "强势": 1.5, "走高": 1.5, "攀升": 1.5,
+		"大涨": 2.5, "飙升": 3.0, "井喷": 3.0, "爆发": 2.5, "暴涨": 3.0,
+	}
+
+	// 负面金融词汇及其权重
+	negativeFinanceWords = map[string]float64{
+		"下跌": 2.0, "跌停": 3.0, "熊市": 3.0, "回调": 1.5, "新低": 2.5,
+		"利空": 2.5, "减持": 2.0, "卖出": 2.0, "看空": 2.0, "亏损": 2.5,
+		"下滑": 2.0, "萎缩": 2.0, "不及预期": 2.5, "疲软": 1.5, "恶化": 2.0,
+		"衰退": 2.0, "跌破": 2.0, "创新低": 3.0, "走弱": 1.5, "下挫": 1.5,
+		"利空消息": 3.0, "收益下降": 2.5, "利润下滑": 2.5, "业绩不佳": 2.5,
+		"垃圾股": 2.0, "风险股": 2.0, "弱势": 1.5, "走低": 1.5, "缩量": 2.5,
+		"大跌": 2.5, "暴跌": 3.0, "崩盘": 3.0, "跳水": 3.0, "重挫": 3.0,
+	}
+
+	// 否定词，用于反转情感极性
+	negationWords = map[string]struct{}{
+		"不": {}, "没": {}, "无": {}, "非": {}, "未": {}, "别": {}, "勿": {},
+	}
+
+	// 程度副词，用于调整情感强度
+	degreeWords = map[string]float64{
+		"非常": 1.8, "极其": 2.2, "太": 1.8, "很": 1.5,
+		"比较": 0.8, "稍微": 0.6, "有点": 0.7, "显著": 1.5,
+		"大幅": 1.8, "急剧": 2.0, "轻微": 0.6, "小幅": 0.7,
+	}
+
+	// 转折词，用于识别情感转折
+	transitionWords = map[string]struct{}{
+		"但是": {}, "然而": {}, "不过": {}, "却": {}, "可是": {},
+	}
+)
+
+// SentimentResult 情感分析结果类型
+type SentimentResult struct {
+	Score         float64       // 情感得分
+	Category      SentimentType // 情感类别
+	PositiveCount int           // 正面词数量
+	NegativeCount int           // 负面词数量
+	Description   string        // 情感描述
+}
+
+// SentimentType 情感类型枚举
+type SentimentType int
+
+const (
+	Positive SentimentType = iota
+	Negative
+	Neutral
+)
+
+// AnalyzeSentiment 判断文本的情感
+func AnalyzeSentiment(text string) SentimentResult {
+	// 初始化得分
+	score := 0.0
+	positiveCount := 0
+	negativeCount := 0
+
+	// 分词（简单按单个字符分割）
+	words := splitWords(text)
+
+	// 检查文本是否包含转折词，并分割成两部分
+	var transitionIndex int
+	var hasTransition bool
+	for i, word := range words {
+		if _, ok := transitionWords[word]; ok {
+			transitionIndex = i
+			hasTransition = true
+			break
+		}
+	}
+
+	// 处理有转折的文本
+	if hasTransition {
+		// 转折前的部分
+		preTransitionWords := words[:transitionIndex]
+		preScore, prePos, preNeg := calculateScore(preTransitionWords)
+
+		// 转折后的部分，权重加倍
+		postTransitionWords := words[transitionIndex+1:]
+		postScore, postPos, postNeg := calculateScore(postTransitionWords)
+		postScore *= 1.5 // 转折后的情感更重要
+
+		score = preScore + postScore
+		positiveCount = prePos + postPos
+		negativeCount = preNeg + postNeg
+	} else {
+		// 没有转折的文本
+		score, positiveCount, negativeCount = calculateScore(words)
+	}
+
+	// 确定情感类别
+	var category SentimentType
+	switch {
+	case score > 1.0:
+		category = Positive
+	case score < -1.0:
+		category = Negative
+	default:
+		category = Neutral
+	}
+
+	return SentimentResult{
+		Score:         score,
+		Category:      category,
+		PositiveCount: positiveCount,
+		NegativeCount: negativeCount,
+		Description:   GetSentimentDescription(category),
+	}
+}
+
+// 计算情感得分
+func calculateScore(words []string) (float64, int, int) {
+	score := 0.0
+	positiveCount := 0
+	negativeCount := 0
+
+	// 遍历每个词，计算情感得分
+	for i, word := range words {
+		// 首先检查是否为程度副词
+		degree, isDegree := degreeWords[word]
+
+		// 检查是否为否定词
+		_, isNegation := negationWords[word]
+
+		// 检查是否为金融正面词
+		if posScore, isPositive := positiveFinanceWords[word]; isPositive {
+			// 检查前一个词是否为否定词或程度副词
+			if i > 0 {
+				prevWord := words[i-1]
+				if _, isNeg := negationWords[prevWord]; isNeg {
+					score -= posScore
+					negativeCount++
+					continue
+				}
+
+				if deg, isDeg := degreeWords[prevWord]; isDeg {
+					score += posScore * deg
+					positiveCount++
+					continue
+				}
+			}
+
+			score += posScore
+			positiveCount++
+			continue
+		}
+
+		// 检查是否为金融负面词
+		if negScore, isNegative := negativeFinanceWords[word]; isNegative {
+			// 检查前一个词是否为否定词或程度副词
+			if i > 0 {
+				prevWord := words[i-1]
+				if _, isNeg := negationWords[prevWord]; isNeg {
+					score += negScore
+					positiveCount++
+					continue
+				}
+
+				if deg, isDeg := degreeWords[prevWord]; isDeg {
+					score -= negScore * deg
+					negativeCount++
+					continue
+				}
+			}
+
+			score -= negScore
+			negativeCount++
+			continue
+		}
+
+		// 处理程度副词（如果后面跟着情感词）
+		if isDegree && i+1 < len(words) {
+			nextWord := words[i+1]
+
+			if posScore, isPositive := positiveFinanceWords[nextWord]; isPositive {
+				score += posScore * degree
+				positiveCount++
+				continue
+			}
+
+			if negScore, isNegative := negativeFinanceWords[nextWord]; isNegative {
+				score -= negScore * degree
+				negativeCount++
+				continue
+			}
+		}
+
+		// 处理否定词（如果后面跟着情感词）
+		if isNegation && i+1 < len(words) {
+			nextWord := words[i+1]
+
+			if posScore, isPositive := positiveFinanceWords[nextWord]; isPositive {
+				score -= posScore
+				negativeCount++
+				continue
+			}
+
+			if negScore, isNegative := negativeFinanceWords[nextWord]; isNegative {
+				score += negScore
+				positiveCount++
+				continue
+			}
+		}
+	}
+
+	return score, positiveCount, negativeCount
+}
+
+// 简单的分词函数，考虑了中文和英文
+func splitWords(text string) []string {
+	x := gojieba.NewJieba()
+	defer x.Free()
+
+	return x.Cut(text, true)
+}
+
+// GetSentimentDescription 获取情感类别的文本描述
+func GetSentimentDescription(category SentimentType) string {
+	switch category {
+	case Positive:
+		return "看涨"
+	case Negative:
+		return "看跌"
+	case Neutral:
+		return "中性"
+	default:
+		return "未知"
+	}
+}
+
+func main() {
+	// 从命令行读取输入
+	reader := bufio.NewReader(os.Stdin)
+	fmt.Println("请输入要分析的股市相关文本（输入exit退出）：")
+
+	for {
+		fmt.Print("> ")
+		text, err := reader.ReadString('\n')
+		if err != nil {
+			fmt.Println("读取输入时出错:", err)
+			continue
+		}
+
+		// 去除换行符
+		text = strings.TrimSpace(text)
+
+		// 检查是否退出
+		if text == "exit" {
+			break
+		}
+
+		// 分析情感
+		result := AnalyzeSentiment(text)
+
+		// 输出结果
+		fmt.Printf("情感分析结果: %s (得分: %.2f, 正面词:%d, 负面词:%d)\n",
+			GetSentimentDescription(result.Category),
+			result.Score,
+			result.PositiveCount,
+			result.NegativeCount)
+	}
+}

backend/data/stock_sentiment_analysis_test.go

@@ -0,0 +1,31 @@
+package data
+
+import (
+	"fmt"
+	"testing"
+)
+
+// @Author spark
+// @Date 2025/6/19 13:05
+// @Desc
+//-----------------------------------------------------------------------------------
+
+func TestAnalyzeSentiment(t *testing.T) {
+	// 分析情感
+	text := " 【调查：韩国近两成中小学生过度使用智能手机或互联网】财联社6月19日电，韩国女性家族部18日公布的一项年度调查结果显示，接受调查的韩国中小学生中，共计约17.3%、即超过21万人使用智能手机或互联网的程度达到了“危险水平”，这意味着他们因过度依赖智能手机或互联网而需要关注或干预，这一比例引人担忧。 (新华社)\n"
+	text = "消息人士称，联合利华（Unilever）正在为Graze零食品牌寻找买家。\n"
+	text = "【韩国未来5年将投入51万亿韩元发展文化产业】 据韩联社，韩国文化体育观光部（文体部）今后5年将投入51万亿韩元（约合人民币2667亿元）预算，落实总统李在明在竞选时期提出的“将韩国打造成全球五大文化强国之一”的承诺。\n"
+	//text = "【油气股持续拉升 国际实业午后涨停】财联社6月19日电，油气股午后持续拉升，国际实业、宝莫股份午后涨停，准油股份、山东墨龙。茂化实华此前涨停，通源石油、海默科技、贝肯能源、中曼石油、科力股份等多股涨超5%。\n"
+	//text = " 【三大指数均跌逾1% 下跌个股近4800只】财联社6月19日电，指数持续走弱，沪指下挫跌逾1.00%，深成指跌1.25%，创业板指跌1.39%。核聚变、风电、军工、食品消费等板块指数跌幅居前，沪深京三市下跌个股近4800只。\n"
+	text = "【银行理财首单网下打新落地】财联社6月20日电，记者从多渠道获悉，光大理财以申报价格17元参与信通电子网下打新，并成功入围有效报价，成为行业内首家参与网下打新的银行理财公司。光大理财工作人员向证券时报记者表示，本次光大理财是以其管理的混合类产品“阳光橙增盈绝对收益策略”参与了此次网下打新，该产品为光大理财“固收+”银行理财产品。资料显示，信通电子成立于1996年，核心产品包括输电线路智能巡检系统、变电站智能辅控系统、移动智能终端及其他产品。根据其招股说明书，信通电子2023、2024年营业收入分别较上年增长19.08%和7.97%，净利润分别较上年增长5.6%和15.11%。 (证券时报)"
+	text = " 【以军称拦截数枚伊朗导弹】财联社6月20日电，据央视新闻报道，以军在贝尔谢巴及周边区域拦截了数枚伊朗导弹，但仍有导弹或拦截残骸落地。以色列国防军发文表示，搜救队伍正在一处“空中物体落地”的所在区域开展工作，公众目前可以离开避难场所。伊朗方面对上述说法暂无回应。"
+	result := AnalyzeSentiment(text)
+
+	// 输出结果
+	fmt.Printf("情感分析结果: %s (得分: %.2f, 正面词:%d, 负面词:%d)\n",
+		result.Description,
+		result.Score,
+		result.PositiveCount,
+		result.NegativeCount)
+
+}

backend/models/models.go

@@ -218,14 +218,15 @@ type Prompt struct {
 
 type Telegraph struct {
 	gorm.Model
-	Time          string          `json:"time"`
+	Time            string          `json:"time"`
-	Content       string          `json:"content"`
+	Content         string          `json:"content"`
-	SubjectTags   []string        `json:"subjects" gorm:"-:all"`
+	SubjectTags     []string        `json:"subjects" gorm:"-:all"`
-	StocksTags    []string        `json:"stocks" gorm:"-:all"`
+	StocksTags      []string        `json:"stocks" gorm:"-:all"`
-	IsRed         bool            `json:"isRed"`
+	IsRed           bool            `json:"isRed"`
-	Url           string          `json:"url"`
+	Url             string          `json:"url"`
-	Source        string          `json:"source"`
+	Source          string          `json:"source"`
-	TelegraphTags []TelegraphTags `json:"tags" gorm:"-:migration;foreignKey:TelegraphId"`
+	TelegraphTags   []TelegraphTags `json:"tags" gorm:"-:migration;foreignKey:TelegraphId"`
+	SentimentResult string          `json:"sentimentResult" gorm:"-:all"`
 }
 type TelegraphTags struct {
 	gorm.Model

frontend/src/App.vue

@@ -16,18 +16,18 @@ import {
   AnalyticsOutline,
   BarChartSharp, EaselSharp,
   ExpandOutline, Flag,
-  Flame, FlameSharp,
+  Flame, FlameSharp, InformationOutline,
   LogoGithub,
   NewspaperOutline,
-  NewspaperSharp,
+  NewspaperSharp, Notifications,
   PowerOutline, Pulse,
   ReorderTwoOutline,
   SettingsOutline, Skull, SkullOutline, SkullSharp,
   SparklesOutline,
   StarOutline,
-  Wallet,
+  Wallet, WarningOutline,
 } from '@vicons/ionicons5'
-import {GetConfig, GetGroupList} from "../wailsjs/go/main/App";
+import {AnalyzeSentiment, GetConfig, GetGroupList} from "../wailsjs/go/main/App";
 
 
 
@@ -547,7 +547,26 @@ onMounted(() => {
       },
     })
     EventsOn("newsPush", (data) => {
-      notification.create({ title: data.time, content: data.content,duration:1000*60 })
+      //console.log(data)
+      if(data.isRed){
+        notification.create({
+          //type:"error",
+         // avatar: () => h(NIcon,{component:Notifications,color:"red"}),
+          title: data.time,
+          content: () => h(NText,{type:"error"}, { default: () => data.content }),
+          meta: () => h(NText,{type:"warning"}, { default: () => data.source}),
+          duration:1000*40,
+        })
+      }else{
+        notification.create({
+          //type:"info",
+          //avatar: () => h(NIcon,{component:Notifications}),
+          title: data.time,
+          content: () => h(NText,{type:"info"}, { default: () => data.content }),
+          meta: () => h(NText,{type:"warning"}, { default: () => data.source}),
+          duration:1000*30 ,
+        })
+      }
     })
   })
 })

frontend/wailsjs/go/main/App.d.ts

@@ -11,6 +11,8 @@ export function AddPrompt(arg1:models.Prompt):Promise<string>;
 
 export function AddStockGroup(arg1:number,arg2:string):Promise<string>;
 
+export function AnalyzeSentiment(arg1:string):Promise<data.SentimentResult>;
+
 export function CheckUpdate():Promise<void>;
 
 export function DelPrompt(arg1:number):Promise<string>;

frontend/wailsjs/go/main/App.js

@@ -18,6 +18,10 @@ export function AddStockGroup(arg1, arg2) {
   return window['go']['main']['App']['AddStockGroup'](arg1, arg2);
 }
 
+export function AnalyzeSentiment(arg1) {
+  return window['go']['main']['App']['AnalyzeSentiment'](arg1);
+}
+
 export function CheckUpdate() {
   return window['go']['main']['App']['CheckUpdate']();
 }

frontend/wailsjs/go/models.ts

@@ -290,6 +290,26 @@ export namespace data {
 	
 	
 	
+	export class SentimentResult {
+	    Score: number;
+	    Category: number;
+	    PositiveCount: number;
+	    NegativeCount: number;
+	    Description: string;
+	
+	    static createFrom(source: any = {}) {
+	        return new SentimentResult(source);
+	    }
+	
+	    constructor(source: any = {}) {
+	        if ('string' === typeof source) source = JSON.parse(source);
+	        this.Score = source["Score"];
+	        this.Category = source["Category"];
+	        this.PositiveCount = source["PositiveCount"];
+	        this.NegativeCount = source["NegativeCount"];
+	        this.Description = source["Description"];
+	    }
+	}
 	export class Settings {
 	    ID: number;
 	    // Go type: time

go.mod

@@ -17,6 +17,7 @@ require (
 	github.com/stretchr/testify v1.10.0
 	github.com/tidwall/gjson v1.14.2
 	github.com/wailsapp/wails/v2 v2.10.1
+	github.com/yanyiwu/gojieba v1.4.6
 	go.uber.org/zap v1.27.0
 	golang.org/x/sys v0.31.0
 	golang.org/x/text v0.23.0

go.sum

@@ -135,6 +135,8 @@ github.com/wailsapp/mimetype v1.4.1 h1:pQN9ycO7uo4vsUUuPeHEYoUkLVkaRntMnHJxVwYhw
 github.com/wailsapp/mimetype v1.4.1/go.mod h1:9aV5k31bBOv5z6u+QP8TltzvNGJPmNJD4XlAL3U+j3o=
 github.com/wailsapp/wails/v2 v2.10.1 h1:QWHvWMXII2nI/nXz77gpPG8P3ehl6zKe+u4su5BWIns=
 github.com/wailsapp/wails/v2 v2.10.1/go.mod h1:zrebnFV6MQf9kx8HI4iAv63vsR5v67oS7GTEZ7Pz1TY=
+github.com/yanyiwu/gojieba v1.4.6 h1:9oKbZijSHBdoTabXK34romSWj4aQLvs+j1ctIQjSxPk=
+github.com/yanyiwu/gojieba v1.4.6/go.mod h1:JUq4DddFVGdHXJHxxepxRmhrKlDpaBxR8O28v6fKYLY=
 github.com/yuin/goldmark v1.4.13/go.mod h1:6yULJ656Px+3vBD8DxQVa3kxgyrAnzto9xy5taEt/CY=
 go.uber.org/goleak v1.3.0 h1:2K3zAYmnTNqV73imy9J1T3WC+gmCePx2hEGkimedGto=
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